Artificial Intelligence Project

DTIC Science & Technology

1990-01-01

Artifcial Intelligence Project at The University of Texas at Austin, University of Texas at Austin, Artificial Intelligence Laboratory AITR84-01. Novak...Texas at Austin, Artificial Intelligence Laboratory A187-52, April 1987. Novak, G. "GLISP: A Lisp-Based Programming System with Data Abstraction...of Texas at Austin, Artificial Intelligence Laboratory AITR85-14.) Rim, Hae-Chang, and Simmons, R. F. "Extracting Data Base Knowledge from Medical

Non-Intrusive Gaze Tracking Using Artificial Neural Networks

DTIC Science & Technology

1994-01-05

We have developed an artificial neural network based gaze tracking, system which can be customized to individual users. A three layer feed forward...empirical analysis of the performance of a large number of artificial neural network architectures for this task. Suggestions for further explorations...for neurally based gaze trackers are presented, and are related to other similar artificial neural network applications such as autonomous road following.

Visiting Scholars Program

DTIC Science & Technology

2016-09-01

other associated grants. 15. SUBJECT TERMS SUNY Poly, STEM, Artificial Intelligence , Command and Control 16. SECURITY CLASSIFICATION OF: 17...neuromorphic system has the potential to be widely used in a high-efficiency artificial intelligence system. Simulation results have indicated that the...novel multiresolution fusion and advanced fusion performance evaluation tool for an Artificial Intelligence based natural language annotation engine for

Magnetic skyrmion-based artificial neuron device

NASA Astrophysics Data System (ADS)

Li, Sai; Kang, Wang; Huang, Yangqi; Zhang, Xichao; Zhou, Yan; Zhao, Weisheng

2017-08-01

Neuromorphic computing, inspired by the biological nervous system, has attracted considerable attention. Intensive research has been conducted in this field for developing artificial synapses and neurons, attempting to mimic the behaviors of biological synapses and neurons, which are two basic elements of a human brain. Recently, magnetic skyrmions have been investigated as promising candidates in neuromorphic computing design owing to their topologically protected particle-like behaviors, nanoscale size and low driving current density. In one of our previous studies, a skyrmion-based artificial synapse was proposed, with which both short-term plasticity and long-term potentiation functions have been demonstrated. In this work, we further report on a skyrmion-based artificial neuron by exploiting the tunable current-driven skyrmion motion dynamics, mimicking the leaky-integrate-fire function of a biological neuron. With a simple single-device implementation, this proposed artificial neuron may enable us to build a dense and energy-efficient spiking neuromorphic computing system.

Optimization with artificial neural network systems - A mapping principle and a comparison to gradient based methods

NASA Technical Reports Server (NTRS)

Leong, Harrison Monfook

1988-01-01

General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.

Artificial-intelligence-based optimization of the management of snow removal assets and resources.

DOT National Transportation Integrated Search

2002-10-01

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent : snow removal asset management system (SRAMS). The system has been evaluated through a case study examining : snow removal from the ...

Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

NASA Astrophysics Data System (ADS)

Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

2018-03-01

The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

The application of hybrid artificial intelligence systems for forecasting

NASA Astrophysics Data System (ADS)

Lees, Brian; Corchado, Juan

1999-03-01

The results to date are presented from an ongoing investigation, in which the aim is to combine the strengths of different artificial intelligence methods into a single problem solving system. The premise underlying this research is that a system which embodies several cooperating problem solving methods will be capable of achieving better performance than if only a single method were employed. The work has so far concentrated on the combination of case-based reasoning and artificial neural networks. The relative merits of artificial neural networks and case-based reasoning problem solving paradigms, and their combination are discussed. The integration of these two AI problem solving methods in a hybrid systems architecture, such that the neural network provides support for learning from past experience in the case-based reasoning cycle, is then presented. The approach has been applied to the task of forecasting the variation of physical parameters of the ocean. Results obtained so far from tests carried out in the dynamic oceanic environment are presented.

An Artificial Intelligence-Based Distance Education System: Artimat

ERIC Educational Resources Information Center

Nabiyev, Vasif; Karal, Hasan; Arslan, Selahattin; Erumit, Ali Kursat; Cebi, Ayca

2013-01-01

The purpose of this study is to evaluate the artificial intelligence-based distance education system called ARTIMAT, which has been prepared in order to improve mathematical problem solving skills of the students, in terms of conceptual proficiency and ease of use with the opinions of teachers and students. The implementation has been performed…

Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

NASA Technical Reports Server (NTRS)

Barnden, John; Srinivas, Kankanahalli

1990-01-01

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. A promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. The current neural net system (Conposit), which performs standard rule-based reasoning, is being modified into a massively parallel case-based reasoning version.

A Game Based e-Learning System to Teach Artificial Intelligence in the Computer Sciences Degree

ERIC Educational Resources Information Center

de Castro-Santos, Amable; Fajardo, Waldo; Molina-Solana, Miguel

2017-01-01

Our students taking the Artificial Intelligence and Knowledge Engineering courses often encounter a large number of problems to solve which are not directly related to the subject to be learned. To solve this problem, we have developed a game based e-learning system. The elected game, that has been implemented as an e-learning system, allows to…

Single Microwave-Photon Detector using an Artificial Lambda-type Three-Level System

DTIC Science & Technology

2016-01-11

Single microwave-photon detector using an artificial Λ-type three- level system Kunihiro Inomata,1∗†, Zhirong Lin,1†, Kazuki Koshino,2, William D...three- level system Kunihiro Inomata,1∗† Zhirong Lin,1† Kazuki Koshino,2 William D. Oliver,3,4 Jaw-Shen Tsai,1 Tsuyoshi Yamamoto,5 Yasunobu Nakamura...single-microwave-photon detector based on the deterministic switching in an artificial Λ-type three- level system implemented using the dressed states of a

What Artificial Intelligence Is Doing for Training.

ERIC Educational Resources Information Center

Kirrane, Peter R.; Kirrane, Diane E.

1989-01-01

Discusses the three areas of research and application of artificial intelligence: (1) robotics, (2) natural language processing, and (3) knowledge-based or expert systems. Focuses on what expert systems can do, especially in the area of training. (JOW)

Tuberculosis disease diagnosis using artificial immune recognition system.

PubMed

Shamshirband, Shahaboddin; Hessam, Somayeh; Javidnia, Hossein; Amiribesheli, Mohsen; Vahdat, Shaghayegh; Petković, Dalibor; Gani, Abdullah; Kiah, Miss Laiha Mat

2014-01-01

There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods. This study is aimed at diagnosing TB using hybrid machine learning approaches. Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm. Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

PubMed Central

Liu, Xiaohui

2013-01-01

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity. PMID:23983638

A Review of Safety and Design Requirements of the Artificial Pancreas.

PubMed

Blauw, Helga; Keith-Hynes, Patrick; Koops, Robin; DeVries, J Hans

2016-11-01

As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components-type 1 diabetes, artificial pancreas, and safety or design-and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.

Development of the CODER System: A Testbed for Artificial Intelligence Methods in Information Retrieval.

ERIC Educational Resources Information Center

Fox, Edward A.

1987-01-01

Discusses the CODER system, which was developed to investigate the application of artificial intelligence methods to increase the effectiveness of information retrieval systems, particularly those involving heterogeneous documents. Highlights include the use of PROLOG programing, blackboard-based designs, knowledge engineering, lexicological…

Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

DTIC Science & Technology

1988-04-13

Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

Artificial Bee Colony Optimization for Short-Term Hydrothermal Scheduling

NASA Astrophysics Data System (ADS)

Basu, M.

2014-12-01

Artificial bee colony optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The algorithm is tested on a multi-reservoir cascaded hydroelectric system having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The algorithm is tested on two hydrothermal multi-reservoir cascaded hydroelectric test systems. The results of the proposed approach are compared with those of differential evolution, evolutionary programming and particle swarm optimization. From numerical results, it is found that the proposed artificial bee colony optimization based approach is able to provide better solution.

Coupling artificial intelligence and numerical computation for engineering design (Invited paper)

NASA Astrophysics Data System (ADS)

Tong, S. S.

1986-01-01

The possibility of combining artificial intelligence (AI) systems and numerical computation methods for engineering designs is considered. Attention is given to three possible areas of application involving fan design, controlled vortex design of turbine stage blade angles, and preliminary design of turbine cascade profiles. Among the AI techniques discussed are: knowledge-based systems; intelligent search; and pattern recognition systems. The potential cost and performance advantages of an AI-based design-generation system are discussed in detail.

Artificial Intelligence Techniques: Applications for Courseware Development.

ERIC Educational Resources Information Center

Dear, Brian L.

1986-01-01

Introduces some general concepts and techniques of artificial intelligence (natural language interfaces, expert systems, knowledge bases and knowledge representation, heuristics, user-interface metaphors, and object-based environments) and investigates ways these techniques might be applied to analysis, design, development, implementation, and…

Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

NASA Astrophysics Data System (ADS)

Salamatova, T.; Zhukov, V.

2017-02-01

The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

List of ARI Conference Papers, Journal Articles, Books, and Book Chapters: 1982-1991

DTIC Science & Technology

1992-10-01

and Engineering Applications of Artificial Intelligence and Expert Systems, Tullahoma, TN. Goehring, D.J., & Hart, R.J. (1985, October). Automated...systems: Computkr-based authoring. Proceedings of the 30th annual meeting of the Artificial Intelligence Society, Dayton, OH. Knapp, D.J., & Pliske, R.M...Moses, F.L. (1984-85) Intelligence vehicle integrated displays. Paper presented at the Conference on Applied Artificial Intelligence , the Data Processing

Rapid Simulation of Blast Wave Propagation in Built Environments Using Coarse-Grain Based Intelligent Modeling Methods

DTIC Science & Technology

2011-04-01

experiments was performed using an artificial neural network to try to capture the nonlinearities. The radial Gaussian artificial neural network system...Modeling Blast-Wave Propagation using Artificial Neural Network Methods‖, in International Journal of Advanced Engineering Informatics, Elsevier

Artificial Intelligence, Expert Systems, Natural Language Interfaces, Knowledge Engineering and the Librarian.

ERIC Educational Resources Information Center

Davies, Jim

This paper begins by examining concepts of artificial intelligence (AI) and discusses various definitions of the concept that have been suggested in the literature. The nesting relationship of expert systems within the broader framework of AI is described, and expert systems are characterized as knowledge-based systems (KBS) which attempt to solve…

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

PubMed

Jun Liu; Fan Zhang; Huang, He Helen

2014-01-01

Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data

PubMed Central

Li, Pengfei; Li, Yan; Guo, Xiucheng

2014-01-01

The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems. PMID:25435870

A red-light running prevention system based on artificial neural network and vehicle trajectory data.

PubMed

Li, Pengfei; Li, Yan; Guo, Xiucheng

2014-01-01

The high frequency of red-light running and complex driving behaviors at the yellow onset at intersections cannot be explained solely by the dilemma zone and vehicle kinematics. In this paper, the author presented a red-light running prevention system which was based on artificial neural networks (ANNs) to approximate the complex driver behaviors during yellow and all-red clearance and serve as the basis of an innovative red-light running prevention system. The artificial neural network and vehicle trajectory are applied to identify the potential red-light runners. The ANN training time was also acceptable and its predicting accurate rate was over 80%. Lastly, a prototype red-light running prevention system with the trained ANN model was described. This new system can be directly retrofitted into the existing traffic signal systems.

Artificial Photosynthesis: Beyond Mimicking Nature

DOE PAGES

Dau, Holger; Fujita, Etsuko; Sun, Licheng

2017-11-13

In this Editorial, Guest Editors Holger Dau, Etsuko Fujita, and Licheng Sun introduce the Special Issue of ChemSusChem on “Artificial Photosynthesis for Sustainable Fuels”. Here, they discuss the need for non-fossil based fuels, introduce both biological and artificial photosynthesis, and outline various important concepts in artificial photosynthesis, including molecular and solid-state catalysts for water oxidation and hydrogen evolution, catalytic CO 2 reduction, and photoelectrochemical systems.

Artificial Photosynthesis: Beyond Mimicking Nature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dau, Holger; Fujita, Etsuko; Sun, Licheng

In this Editorial, Guest Editors Holger Dau, Etsuko Fujita, and Licheng Sun introduce the Special Issue of ChemSusChem on “Artificial Photosynthesis for Sustainable Fuels”. Here, they discuss the need for non-fossil based fuels, introduce both biological and artificial photosynthesis, and outline various important concepts in artificial photosynthesis, including molecular and solid-state catalysts for water oxidation and hydrogen evolution, catalytic CO 2 reduction, and photoelectrochemical systems.

Artificial Olfactory System for Trace Identification of Explosive Vapors Realized by Optoelectronic Schottky Sensing.

PubMed

Guo, Linjuan; Yang, Zheng; Dou, Xincun

2017-02-01

A rapid, ultrasensitive artificial olfactory system based on an individual optoelectronic Schottky junction is demonstrated for the discriminative detection of explosive vapors, including military explosives and improvised explosives. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Developing a scalable artificial photosynthesis technology through nanomaterials by design

NASA Astrophysics Data System (ADS)

Lewis, Nathan S.

2016-12-01

An artificial photosynthetic system that directly produces fuels from sunlight could provide an approach to scalable energy storage and a technology for the carbon-neutral production of high-energy-density transportation fuels. A variety of designs are currently being explored to create a viable artificial photosynthetic system, and the most technologically advanced systems are based on semiconducting photoelectrodes. Here, I discuss the development of an approach that is based on an architecture, first conceived around a decade ago, that combines arrays of semiconducting microwires with flexible polymeric membranes. I highlight the key steps that have been taken towards delivering a fully functional solar fuels generator, which have exploited advances in nanotechnology at all hierarchical levels of device construction, and include the discovery of earth-abundant electrocatalysts for fuel formation and materials for the stabilization of light absorbers. Finally, I consider the remaining scientific and engineering challenges facing the fulfilment of an artificial photosynthetic system that is simultaneously safe, robust, efficient and scalable.

Knowledge Based Systems (KBS) Verification, Validation, Evaluation, and Testing (VVE&T) Bibliography: Topical Categorization

DTIC Science & Technology

2003-03-01

Different?," Jour. of Experimental & Theoretical Artificial Intelligence, Special Issue on Al for Systems Validation and Verification, 12(4), 2000, pp...Hamilton, D., " Experiences in Improving the State of Practice in Verification and Validation of Knowledge-Based Systems," Workshop Notes of the AAAI...Unsuspected Power of the Standard Turing Test," Jour. of Experimental & Theoretical Artificial Intelligence., 12, 2000, pp3 3 1-3 4 0 . [30] Gaschnig

[Artificial intelligence--the knowledge base applied to nephrology].

PubMed

Sancipriano, G P

2005-01-01

The idea that efficacy efficiency, and quality in medicine could not be reached without sorting the huge knowledge of medical and nursing science is very common. Engineers and computer scientists have developed medical software with great prospects for success, but currently these software applications are not so useful in clinical practice. The medical doctor and the trained nurse live the 'information age' in many daily activities, but the main benefits are not so widespread in working activities. Artificial intelligence and, particularly, export systems charm health staff because of their potential. The first part of this paper summarizes the characteristics of 'weak artificial intelligence' and of expert systems important in clinical practice. The second part discusses medical doctors' requirements and the current nephrologic knowledge bases available for artificial intelligence development.

Imaging dipole flow sources using an artificial lateral-line system made of biomimetic hair flow sensors

PubMed Central

Dagamseh, Ahmad; Wiegerink, Remco; Lammerink, Theo; Krijnen, Gijs

2013-01-01

In Nature, fish have the ability to localize prey, school, navigate, etc., using the lateral-line organ. Artificial hair flow sensors arranged in a linear array shape (inspired by the lateral-line system (LSS) in fish) have been applied to measure airflow patterns at the sensor positions. Here, we take advantage of both biomimetic artificial hair-based flow sensors arranged as LSS and beamforming techniques to demonstrate dipole-source localization in air. Modelling and measurement results show the artificial lateral-line ability to image the position of dipole sources accurately with estimation error of less than 0.14 times the array length. This opens up possibilities for flow-based, near-field environment mapping that can be beneficial to, for example, biologists and robot guidance applications. PMID:23594816

Overload control of artificial gravity facility using spinning tether system for high eccentricity transfer orbits

NASA Astrophysics Data System (ADS)

Gou, Xing-wang; Li, Ai-jun; Tian, Hao-chang; Wang, Chang-qing; Lu, Hong-shi

2018-06-01

As the major part of space life supporting systems, artificial gravity requires further study before it becomes mature. Spinning tether system is a good alternative solution to provide artificial gravity for the whole spacecraft other than additional devices, and its longer tether length could significantly reduce spinning velocity and thus enhance comfortability. An approximated overload-based feedback method is proposed to provide estimated spinning velocity signals for controller, so that gravity level could be accurately controlled without complicated GPS modules. System behavior in high eccentricity transfer orbits is also studied to give a complete knowledge of the spinning stabilities. The application range of the proposed method is studied in various orbit cases and spinning velocities, indicating that it is accurate and reliable for most of the mission phases especially for the final constant gravity level phase. In order to provide stable gravity level for transfer orbit missions, a sliding mode controller based on estimated angular signals is designed for closed-loop control. Numerical results indicate that the combination of overload-based feedback and sliding mode controller could satisfy most of the long-term artificial gravity missions. It is capable of forming flexible gravity environment in relatively good accuracy even in the lowest possible orbital radiuses and high eccentricity orbits of crewed space missions. The proposed scheme provides an effective tether solution for the artificial gravity construction in interstellar travel.

Clonal Selection Based Artificial Immune System for Generalized Pattern Recognition

NASA Technical Reports Server (NTRS)

Huntsberger, Terry

2011-01-01

The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.

Northeast Artificial Intelligence Consortium annual report. Volume 2. 1988. Discussing, using, and recognizing plans (NLP). Interim report, January-December 1988

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shapiro, S.C.; Woolf, B.

The Northeast Artificial Intelligence Consortium (NAIC) was created by the Air Force Systems Command, Rome Air Development Center, and the Office of Scientific Research. Its purpose is to conduct pertinent research in artificial intelligence and to perform activities ancillary to this research. This report describes progress that has been made in the fourth year of the existence of the NAIC on the technical research tasks undertaken at the member universities. The topics covered in general are: versatile expert system for equipment maintenance, distributed AI for communications system control, automatic photointerpretation, time-oriented problem solving, speech understanding systems, knowledge base maintenance, hardwaremore » architectures for very large systems, knowledge-based reasoning and planning, and a knowledge acquisition, assistance, and explanation system. The specific topic for this volume is the recognition of plans expressed in natural language, followed by their discussion and use.« less

Artificial-neural-network-based failure detection and isolation

NASA Astrophysics Data System (ADS)

Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

1998-03-01

This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

Multisensor system and artificial intelligence in housing for the elderly.

PubMed

Chan, M; Bocquet, H; Campo, E; Val, T; Estève, D; Pous, J

1998-01-01

To improve the safety of a growing proportion of elderly and disabled people in the developed countries, a multisensor system based on Artificial Intelligence (AI), Advanced Telecommunications (AT) and Information Technology (IT) has been devised and fabricated. Thus, the habits and behaviours of these populations will be recorded without disturbing their daily activities. AI will diagnose any abnormal behavior or change and the system will warn the professionals. Gerontology issues are presented together with the multisensor system, the AI-based learning and diagnosis methodology and the main functionalities.

Computational aerodynamics and artificial intelligence

NASA Technical Reports Server (NTRS)

Mehta, U. B.; Kutler, P.

1984-01-01

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics.

Knowledge-Based Aircraft Automation: Managers Guide on the use of Artificial Intelligence for Aircraft Automation and Verification and Validation Approach for a Neural-Based Flight Controller

NASA Technical Reports Server (NTRS)

Broderick, Ron

1997-01-01

The ultimate goal of this report was to integrate the powerful tools of artificial intelligence into the traditional process of software development. To maintain the US aerospace competitive advantage, traditional aerospace and software engineers need to more easily incorporate the technology of artificial intelligence into the advanced aerospace systems being designed today. The future goal was to transition artificial intelligence from an emerging technology to a standard technology that is considered early in the life cycle process to develop state-of-the-art aircraft automation systems. This report addressed the future goal in two ways. First, it provided a matrix that identified typical aircraft automation applications conducive to various artificial intelligence methods. The purpose of this matrix was to provide top-level guidance to managers contemplating the possible use of artificial intelligence in the development of aircraft automation. Second, the report provided a methodology to formally evaluate neural networks as part of the traditional process of software development. The matrix was developed by organizing the discipline of artificial intelligence into the following six methods: logical, object representation-based, distributed, uncertainty management, temporal and neurocomputing. Next, a study of existing aircraft automation applications that have been conducive to artificial intelligence implementation resulted in the following five categories: pilot-vehicle interface, system status and diagnosis, situation assessment, automatic flight planning, and aircraft flight control. The resulting matrix provided management guidance to understand artificial intelligence as it applied to aircraft automation. The approach taken to develop a methodology to formally evaluate neural networks as part of the software engineering life cycle was to start with the existing software quality assurance standards and to change these standards to include neural network development. The changes were to include evaluation tools that can be applied to neural networks at each phase of the software engineering life cycle. The result was a formal evaluation approach to increase the product quality of systems that use neural networks for their implementation.

Artificial synapse network on inorganic proton conductor for neuromorphic systems.

PubMed

Zhu, Li Qiang; Wan, Chang Jin; Guo, Li Qiang; Shi, Yi; Wan, Qing

2014-01-01

The basic units in our brain are neurons, and each neuron has more than 1,000 synapse connections. Synapse is the basic structure for information transfer in an ever-changing manner, and short-term plasticity allows synapses to perform critical computational functions in neural circuits. Therefore, the major challenge for the hardware implementation of neuromorphic computation is to develop artificial synapse network. Here in-plane lateral-coupled oxide-based artificial synapse network coupled by proton neurotransmitters are self-assembled on glass substrates at room-temperature. A strong lateral modulation is observed due to the proton-related electrical-double-layer effect. Short-term plasticity behaviours, including paired-pulse facilitation, dynamic filtering and spatiotemporally correlated signal processing are mimicked. Such laterally coupled oxide-based protonic/electronic hybrid artificial synapse network proposed here is interesting for building future neuromorphic systems.

Experiments with microcomputer-based artificial intelligence environments

USGS Publications Warehouse

Summers, E.G.; MacDonald, R.A.

1988-01-01

The U.S. Geological Survey (USGS) has been experimenting with the use of relatively inexpensive microcomputers as artificial intelligence (AI) development environments. Several AI languages are available that perform fairly well on desk-top personal computers, as are low-to-medium cost expert system packages. Although performance of these systems is respectable, their speed and capacity limitations are questionable for serious earth science applications foreseen by the USGS. The most capable artificial intelligence applications currently are concentrated on what is known as the "artificial intelligence computer," and include Xerox D-series, Tektronix 4400 series, Symbolics 3600, VAX, LMI, and Texas Instruments Explorer. The artificial intelligence computer runs expert system shells and Lisp, Prolog, and Smalltalk programming languages. However, these AI environments are expensive. Recently, inexpensive 32-bit hardware has become available for the IBM/AT microcomputer. USGS has acquired and recently completed Beta-testing of the Gold Hill Systems 80386 Hummingboard, which runs Common Lisp on an IBM/AT microcomputer. Hummingboard appears to have the potential to overcome many of the speed/capacity limitations observed with AI-applications on standard personal computers. USGS is a Beta-test site for the Gold Hill Systems GoldWorks expert system. GoldWorks combines some high-end expert system shell capabilities in a medium-cost package. This shell is developed in Common Lisp, runs on the 80386 Hummingboard, and provides some expert system features formerly available only on AI-computers including frame and rule-based reasoning, on-line tutorial, multiple inheritance, and object-programming. ?? 1988 International Association for Mathematical Geology.

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

NASA Technical Reports Server (NTRS)

Fayyad, Usama M. (Editor); Uthurusamy, Ramasamy (Editor)

1993-01-01

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Administrators Gaming Test- and Observation-Based Teacher Evaluation Methods: To Conform To or Confront the System

ERIC Educational Resources Information Center

Geiger, Tray J.; Amrein-Beardsley, Audrey

2017-01-01

In this commentary, we discuss three types of data manipulations that can occur within teacher evaluation methods: artificial inflation, artificial deflation, and artificial conflation. These types of manipulation are more popularly known in the education profession as instances of Campbell's Law (1976), which states that the higher the…

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Engineering artificial cells by combining HeLa-based cell-free expression and ultra-thin double emulsion template

PubMed Central

Ho, Kwun Yin; Murray, Victoria L.; Liu, Allen P.

2015-01-01

Generation of artificial cells provides the bridge needed to cover the gap between studying the complexity of biological processes in whole cells and studying these same processes in an in vitro reconstituted system. Artificial cells are defined as the encapsulation of biologically active material in a biological or synthetic membrane. Here, we describe a robust and general method to produce artificial cells for the purpose of mimicking one or more behaviors of a cell. A microfluidic double emulsion system is used to encapsulate a mammalian cell free expression system that is able to express membrane proteins into the bilayer or soluble proteins inside the vesicles. The development of a robust platform that allows the assembly of artificial cells is valuable in understanding subcellular functions and emergent behaviors in a more cell-like environment as well as for creating novel signaling pathways to achieve specific cellular behaviors. PMID:25997354

Artificial immune system algorithm in VLSI circuit configuration

NASA Astrophysics Data System (ADS)

Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd

2017-08-01

In artificial intelligence, the artificial immune system is a robust bio-inspired heuristic method, extensively used in solving many constraint optimization problems, anomaly detection, and pattern recognition. This paper discusses the implementation and performance of artificial immune system (AIS) algorithm integrated with Hopfield neural networks for VLSI circuit configuration based on 3-Satisfiability problems. Specifically, we emphasized on the clonal selection technique in our binary artificial immune system algorithm. We restrict our logic construction to 3-Satisfiability (3-SAT) clauses in order to outfit with the transistor configuration in VLSI circuit. The core impetus of this research is to find an ideal hybrid model to assist in the VLSI circuit configuration. In this paper, we compared the artificial immune system (AIS) algorithm (HNN-3SATAIS) with the brute force algorithm incorporated with Hopfield neural network (HNN-3SATBF). Microsoft Visual C++ 2013 was used as a platform for training, simulating and validating the performances of the proposed network. The results depict that the HNN-3SATAIS outperformed HNN-3SATBF in terms of circuit accuracy and CPU time. Thus, HNN-3SATAIS can be used to detect an early error in the VLSI circuit design.

Strategies for Efficient Charge Separation and Transfer in Artificial Photosynthesis of Solar Fuels.

PubMed

Xu, Yuxing; Li, Ailong; Yao, Tingting; Ma, Changtong; Zhang, Xianwen; Shah, Jafar Hussain; Han, Hongxian

2017-11-23

Converting sunlight to solar fuels by artificial photosynthesis is an innovative science and technology for renewable energy. Light harvesting, photogenerated charge separation and transfer (CST), and catalytic reactions are the three primary steps in the processes involved in the conversion of solar energy to chemical energy (SE-CE). Among the processes, CST is the key "energy pump and delivery" step in determining the overall solar-energy conversion efficiency. Efficient CST is always high priority in designing and assembling artificial photosynthesis systems for solar-fuel production. This Review not only introduces the fundamental strategies for CST but also the combinatory application of these strategies to five types of the most-investigated semiconductor-based artificial photosynthesis systems: particulate, Z-scheme, hybrid, photoelectrochemical, and photovoltaics-assisted systems. We show that artificial photosynthesis systems with high SE-CE efficiency can be rationally designed and constructed through combinatory application of these strategies, setting a promising blueprint for the future of solar fuels. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An automated diagnosis system of liver disease using artificial immune and genetic algorithms.

PubMed

Liang, Chunlin; Peng, Lingxi

2013-04-01

The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

STRUTEX: A prototype knowledge-based system for initially configuring a structure to support point loads in two dimensions

NASA Technical Reports Server (NTRS)

Rogers, James L.; Feyock, Stefan; Sobieszczanski-Sobieski, Jaroslaw

1988-01-01

The purpose of this research effort is to investigate the benefits that might be derived from applying artificial intelligence tools in the area of conceptual design. Therefore, the emphasis is on the artificial intelligence aspects of conceptual design rather than structural and optimization aspects. A prototype knowledge-based system, called STRUTEX, was developed to initially configure a structure to support point loads in two dimensions. This system combines numerical and symbolic processing by the computer with interactive problem solving aided by the vision of the user by integrating a knowledge base interface and inference engine, a data base interface, and graphics while keeping the knowledge base and data base files separate. The system writes a file which can be input into a structural synthesis system, which combines structural analysis and optimization.

Quality assurance paradigms for artificial intelligence in modelling and simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oren, T.I.

1987-04-01

New classes of quality assurance concepts and techniques are required for the advanced knowledge-processing paradigms (such as artificial intelligence, expert systems, or knowledge-based systems) and the complex problems that only simulative systems can cope with. A systematization of quality assurance problems as well as examples are given to traditional and cognizant quality assurance techniques in traditional and cognizant modelling and simulation.

Standard representation and unified stability analysis for dynamic artificial neural network models.

PubMed

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

A critical benefit analysis of artificial gravity as a microgravity countermeasure

NASA Astrophysics Data System (ADS)

Kaderka, Justin; Young, Laurence R.; Paloski, William H.

2010-11-01

Deconditioning of astronauts during long duration spaceflight, especially with regard to the cardiovascular, musculo-skeletal, and neurological systems, is a well-recognized problem that has stimulated significant investments in countermeasure research over the past five decades. Because of its potential salutary effects on all of these systems, artificial gravity via centrifugation has been one of the most persistently discussed countermeasures; however, to date, few studies have tested its efficacy, particularly in comparison to other, system-specific countermeasures. This paper reports results of a meta-analysis we performed to compare previously published results from artificial gravity studies with those from studies utilizing traditional countermeasures, such as resistive exercise, aerobic exercise, lower body negative pressure (LBNP), or some variation of these countermeasures. Published and non-published literature involving human bed rest and immersion studies, human non-bed rest studies, and flight data were examined. Our analyses were confounded by differences in research design from study to study, including subject selection criteria, deconditioning paradigm, physiological systems assessed, and dependent measures employed. Nevertheless we were able to draw comparisons between studies that had some consistency across these variables. Results indicate that for prolonged spaceflight an artificial gravity-based countermeasure may provide benefits equivalent to traditional countermeasures for the cardiovascular system. Too few comparable studies have been performed to draw any conclusions for the musculo-skeletal system. Gaps in the current knowledge of artificial gravity are identified and provide the basis for a discussion of future topics for ground-based research using this countermeasure.

Monitoring Artificial Pancreas Trials Through Agent-based Technologies

PubMed Central

Scarpellini, Stefania; Di Palma, Federico; Toffanin, Chiara; Del Favero, Simone; Magni, Lalo; Bellazzi, Riccardo

2014-01-01

The increase in the availability and reliability of network connections lets envision systems supporting a continuous remote monitoring of clinical parameters useful either for overseeing chronic diseases or for following clinical trials involving outpatients. We report here the results achieved by a telemedicine infrastructure that has been linked to an artificial pancreas platform and used during a trial of the AP@home project, funded by the European Union. The telemedicine infrastructure is based on a multiagent paradigm and is able to deliver to the clinic any information concerning the patient status and the operation of the artificial pancreas. A web application has also been developed, so that the clinic staff and the researchers involved in the design of the blood glucose control algorithms are able to follow the ongoing experiments. Albeit the duration of the experiments in the trial discussed in the article was limited to only 2 days, the system proved to be successful for monitoring patients, in particular overnight when the patients are sleeping. Based on that outcome we can conclude that the infrastructure is suitable for the purpose of accomplishing an intelligent monitoring of an artificial pancreas either during longer trials or whenever that system will be used as a routine treatment. PMID:24876570

Rule based artificial intelligence expert system for determination of upper extremity impairment rating.

PubMed

Lim, I; Walkup, R K; Vannier, M W

1993-04-01

Quantitative evaluation of upper extremity impairment, a percentage rating most often determined using a rule based procedure, has been implemented on a personal computer using an artificial intelligence, rule-based expert system (AI system). In this study, the rules given in Chapter 3 of the AMA Guides to the Evaluation of Permanent Impairment (Third Edition) were used to develop such an AI system for the Apple Macintosh. The program applies the rules from the Guides in a consistent and systematic fashion. It is faster and less error-prone than the manual method, and the results have a higher degree of precision, since intermediate values are not truncated.

Rapid prototyping facility for flight research in artificial-intelligence-based flight systems concepts

NASA Technical Reports Server (NTRS)

Duke, E. L.; Regenie, V. A.; Deets, D. A.

1986-01-01

The Dryden Flight Research Facility of the NASA Ames Research Facility of the NASA Ames Research Center is developing a rapid prototyping facility for flight research in flight systems concepts that are based on artificial intelligence (AI). The facility will include real-time high-fidelity aircraft simulators, conventional and symbolic processors, and a high-performance research aircraft specially modified to accept commands from the ground-based AI computers. This facility is being developed as part of the NASA-DARPA automated wingman program. This document discusses the need for flight research and for a national flight research facility for the rapid prototyping of AI-based avionics systems and the NASA response to those needs.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Modular Extended-Stay HyperGravity Facility Design Concept: An Artificial-Gravity Space-Settlement Ground Analogue

NASA Technical Reports Server (NTRS)

Dorais, Gregory A.

2015-01-01

This document defines the design concept for a ground-based, extended-stay hypergravity facility as a precursor for space-based artificial-gravity facilities that extend the permanent presence of both human and non-human life beyond Earth in artificial-gravity settlements. Since the Earth's current human population is stressing the environment and the resources off-Earth are relatively unlimited, by as soon as 2040 more than one thousand people could be living in Earthorbiting artificial-gravity habitats. Eventually, the majority of humanity may live in artificialgravity habitats throughout this solar system as well as others, but little is known about the longterm (multi-generational) effects of artificial-gravity habitats on people, animals, and plants. In order to extend life permanently beyond Earth, it would be useful to create an orbiting space facility that generates 1g as well as other gravity levels to rigorously address the numerous challenges of such an endeavor. Before doing so, developing a ground-based artificial-gravity facility is a reasonable next step. Just as the International Space Station is a microgravity research facility, at a small fraction of the cost and risk a ground-based artificial-gravity facility can begin to address a wide-variety of the artificial-gravity life-science questions and engineering challenges requiring long-term research to enable people, animals, and plants to live off-Earth indefinitely.

An architecture for rapid prototyping of control schemes for artificial ventricles.

PubMed

Ficola, Antonio; Pagnottelli, Stefano; Valigi, Paolo; Zoppitelli, Maurizio

2004-01-01

This paper presents an experimental system aimed at rapid prototyping of feedback control schemes for ventricular assist devices, and artificial ventricles in general. The system comprises a classical mock circulatory system, an actuated bellow-based ventricle chamber, and a software architecture for control schemes implementation and experimental data acquisition, visualization and storing. Several experiments have been carried out, showing good performance of ventricular pressure tracking control schemes.

Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

NASA Technical Reports Server (NTRS)

Kellner, A.

1987-01-01

Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems.

Information Processing Research.

DTIC Science & Technology

1986-09-01

Kuroe. The 3D MOSAIC Scene Understanding System. In Alan Bundy, Editor, Proceedings of the Eighth International Joint Conference on Artificial ... Artificial Jntelligencel7(1-3):409-460, August, 1981. Given a single picture which is a projection of a three-dimensional scene onto the two...values are detected as outliers by computing the distribution of values over a sliding 80 msec window. During the third pass (based on artificial

A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems

ERIC Educational Resources Information Center

Gomiero, Tiziano; Croce, Luigi; Grossi, Enzo; Luc, De Vreese; Buscema, Massimo; Mantesso, Ulrico; De Bastiani, Elisa

2011-01-01

The aim of this paper is to present a shortened version of the SIS (support intensity scale) obtained by the application of mathematical models and instruments, adopting special algorithms based on the most recent developments in artificial adaptive systems. All the variables of SIS applied to 1,052 subjects with ID (intellectual disabilities)…

Nano-bio assemblies for artificial light harvesting systems

NASA Astrophysics Data System (ADS)

Bain, Dipankar; Maity, Subarna; Patra, Amitava

2018-02-01

Ultrasmall fluorescent gold nanoclusters (Au NCs) have drawn considerable research interest owing to their molecular like properties such as d-sp and sp-sp transitions, and intense fluorescence. Fluorescent Au NCs have especial attraction in biological system owing to their biocompatibility and high photostability. Recently, several strategies have been adapted to design an artificial light-harvesting system using Au NCs for potential applications. Here, we have designed Au nanoclusters based dsDNA (double stranded deoxyribonucleic acid) nano assemblies where the Au nanocluster is covalently attached with Alexa Fluor 488 (A488) dye tagged dsDNA. Investigation reveals that the incorporation of Ag+ into dsDNA enhances the rate of energy transfer from A488 to Au NCs. In addition cadmium telluride quantum dot (CdTe QDs) based Au NCs hybrid material shows the significant enhancement of energy transfer 35% to 83% with changing the capping ligand of Au NCs from glutathione (GSH) to bovine serum albumin (BSA) protein. Another hybrid system is developed using carbon dots and dye encapsulated BSA-protein capped Au NCs for efficient light harvesting system with 83% energy transfer efficiency. Thus, Au NCs base nano bio assemblies may open up new possibilities for the construction of artificial light harvesting system.

A development framework for artificial intelligence based distributed operations support systems

NASA Technical Reports Server (NTRS)

Adler, Richard M.; Cottman, Bruce H.

1990-01-01

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself.

Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System.

PubMed

Duan, Litian; Wang, Zizhong John; Duan, Fu

2016-11-16

In the multiple-reader environment (MRE) of radio frequency identification (RFID) system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS) is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression) quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.

Geometric Distribution-Based Readers Scheduling Optimization Algorithm Using Artificial Immune System

PubMed Central

Duan, Litian; Wang, Zizhong John; Duan, Fu

2016-01-01

In the multiple-reader environment (MRE) of radio frequency identification (RFID) system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS) is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression) quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range. PMID:27854342

An intelligent remote monitoring system for artificial heart.

PubMed

Choi, Jaesoon; Park, Jun W; Chung, Jinhan; Min, Byoung G

2005-12-01

A web-based database system for intelligent remote monitoring of an artificial heart has been developed. It is important for patients with an artificial heart implant to be discharged from the hospital after an appropriate stabilization period for better recovery and quality of life. Reliable continuous remote monitoring systems for these patients with life support devices are gaining practical meaning. The authors have developed a remote monitoring system for this purpose that consists of a portable/desktop monitoring terminal, a database for continuous recording of patient and device status, a web-based data access system with which clinicians can access real-time patient and device status data and past history data, and an intelligent diagnosis algorithm module that noninvasively estimates blood pump output and makes automatic classification of the device status. The system has been tested with data generation emulators installed on remote sites for simulation study, and in two cases of animal experiments conducted at remote facilities. The system showed acceptable functionality and reliability. The intelligence algorithm also showed acceptable practicality in an application to animal experiment data.

The artificial pancreas: evaluating risk of hypoglycaemia following errors that can be expected with prolonged at-home use.

PubMed

Wolpert, H; Kavanagh, M; Atakov-Castillo, A; Steil, G M

2016-02-01

Artificial pancreas systems show benefit in closely monitored at-home studies, but may not have sufficient power to assess safety during infrequent, but expected, system or user errors. The aim of this study was to assess the safety of an artificial pancreas system emulating the β-cell when the glucose value used for control is improperly calibrated and participants forget to administer pre-meal insulin boluses. Artificial pancreas control was performed in a clinic research centre on three separate occasions each lasting from 10 p.m. to 2 p.m. Sensor glucose values normally used for artificial pancreas control were replaced with scaled blood glucose values calculated to be 20% lower than, equal to or 33% higher than the true blood glucose. Safe control was defined as blood glucose between 3.9 and 8.3 mmol/l. Artificial pancreas control resulted in fasting scaled blood glucose values not different from target (6.67 mmol/l) at any scaling factor. Meal control with scaled blood glucose 33% higher than blood glucose resulted in supplemental carbohydrate to prevent hypoglycaemia in four of six participants during breakfast, and one participant during the night. In all instances, scaled blood glucose reported blood glucose as safe. Outpatient trials evaluating artificial pancreas performance based on sensor glucose may not detect hypoglycaemia when sensor glucose reads higher than blood glucose. Because these errors are expected to occur, in-hospital artificial pancreas studies using supplemental carbohydrate in anticipation of hypoglycaemia, which allow safety to be assessed in a controlled non-significant environment should be considered as an alternative. Inpatient studies provide a definitive alternative to model-based computer simulations and can be conducted in parallel with closely monitored outpatient artificial pancreas studies used to assess benefit. © 2015 The Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

Recognising promoter sequences using an artificial immune system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cooke, D.E.; Hunt, J.E.

1995-12-31

We have developed an artificial immune system (AIS) which is based on the human immune system. The AIS possesses an adaptive learning mechanism which enables antibodies to emerge which can be used for classification tasks. In this paper, we describe how the AIS has been used to evolve antibodies which can classify promoter containing and promoter negative DNA sequences. The DNA sequences used for teaching were 57 nucleotides in length and contained procaryotic promoters. The system classified previously unseen DNA sequences with an accuracy of approximately 90%.

A survey of artificial immune system based intrusion detection.

PubMed

Yang, Hua; Li, Tao; Hu, Xinlei; Wang, Feng; Zou, Yang

2014-01-01

In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted.

Use of artificial intelligence to identify cardiovascular compromise in a model of hemorrhagic shock.

PubMed

Glass, Todd F; Knapp, Jason; Amburn, Philip; Clay, Bruce A; Kabrisky, Matt; Rogers, Steven K; Garcia, Victor F

2004-02-01

To determine whether a prototype artificial intelligence system can identify volume of hemorrhage in a porcine model of controlled hemorrhagic shock. Prospective in vivo animal model of hemorrhagic shock. Research foundation animal surgical suite; computer laboratories of collaborating industry partner. Nineteen, juvenile, 25- to 35-kg, male and female swine. Anesthetized animals were instrumented for arterial and systemic venous pressure monitoring and blood sampling, and a splenectomy was performed. Following a 1-hr stabilization period, animals were hemorrhaged in aliquots to 10, 20, 30, 35, 40, 45, and 50% of total blood volume with a 10-min recovery between each aliquot. Data were downloaded directly from a commercial monitoring system into a proprietary PC-based software package for analysis. Arterial and venous blood gas values, glucose, and cardiac output were collected at specified intervals. Electrocardiogram, electroencephalogram, mixed venous oxygen saturation, temperature (core and blood), mean arterial pressure, pulmonary artery pressure, central venous pressure, pulse oximetry, and end-tidal CO(2) were continuously monitored and downloaded. Seventeen of 19 animals (89%) died as a direct result of hemorrhage. Stored data streams were analyzed by the prototype artificial intelligence system. For this project, the artificial intelligence system identified and compared three electrocardiographic features (R-R interval, QRS amplitude, and R-S interval) from each of nine unknown samples of the QRS complex. We found that the artificial intelligence system, trained on only three electrocardiographic features, identified hemorrhage volume with an average accuracy of 91% (95% confidence interval, 84-96%). These experiments demonstrate that an artificial intelligence system, based solely on the analysis of QRS amplitude, R-R interval, and R-S interval of an electrocardiogram, is able to accurately identify hemorrhage volume in a porcine model of lethal hemorrhagic shock. We suggest that this technology may represent a noninvasive means of assessing the physiologic state during and immediately following hemorrhage. Point of care application of this technology may improve outcomes with earlier diagnosis and better titration of therapy of shock.

Functionally graded bio-ceramic reinforced PVA hydrogel composites for knee joint artificial cartilages

NASA Astrophysics Data System (ADS)

Kumar, G. C. Mohan

2018-04-01

Research progress in materials science for bio-based materials for cartilage repair or supportive to host tissue has become a fashionable, worldwide. Few efforts in biomedical engineering has attempted in the development of newer biomaterials successfully. Bio ceramics, a class of materials been used in particulate form as a reinforcement with polymers those ensure its biocompatibility. Every artificial biomedical system has to meet the minimum in Vitro requirements for successful application. Equally the biological behavior of normal and diseased tissues is also essential to understand the artificial systems to human body.

Artificial intelligence in hematology.

PubMed

Zini, Gina

2005-10-01

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.

Artificial “ping-pong” cascade of PIWI-interacting RNA in silkworm cells

PubMed Central

Shoji, Keisuke; Suzuki, Yutaka; Sugano, Sumio; Shimada, Toru; Katsuma, Susumu

2017-01-01

PIWI-interacting RNAs (piRNAs) play essential roles in the defense system against selfish elements in animal germline cells by cooperating with PIWI proteins. A subset of piRNAs is predicted to be generated via the “ping-pong” cascade, which is mainly controlled by two different PIWI proteins. Here we established a cell-based artificial piRNA production system using a silkworm ovarian cultured cell line that is believed to possess a complete piRNA pathway. In addition, we took advantage of a unique silkworm sex-determining one-to-one ping-pong piRNA pair, which enabled us to precisely monitor the behavior of individual artificial piRNAs. With this novel strategy, we successfully generated artificial piRNAs against endogenous protein-coding genes via the expected back-and-forth traveling mechanism. Furthermore, we detected “primary” piRNAs from the upstream region of the artificial “ping-pong” site in the endogenous gene. This artificial piRNA production system experimentally confirms the existence of the “ping-pong” cascade of piRNAs. Also, this system will enable us to identify the factors involved in both, or each, of the “ping” and “pong” cascades and the sequence features that are required for efficient piRNA production. PMID:27777367

Towards Methodologies for Building Knowledge-Based Instructional Systems.

ERIC Educational Resources Information Center

Duchastel, Philippe

1992-01-01

Examines the processes involved in building instructional systems that are based on artificial intelligence and hypermedia technologies. Traditional instructional systems design methodology is discussed; design issues including system architecture and learning strategies are addressed; and a new methodology for building knowledge-based…

Method for improving product yields in an anionic metalloporphyrin-based artificial photosynthesis system

DOEpatents

Shelnutt, John A.

1986-01-01

A method for improving product yields in an anionic metalloporphyrin-based artificial photosynthesis system for hydrogen generation which comprises forming an aqueous solution comprising an electron donor, methylviologen, and certain metalloporphyrins and metallochlorins, and irradiating said aqueous solution with light in the presence of a catalyst. In the photosynthesis process, solar energy is collected and stored in the form of a gas hydrogen. Ligands attached above and below the metalloporphyrin and metallochlorin plane are capable of sterically blocking photochemically inactive electrostatically bound .pi.--.pi. complexes which can develop.

Method for improving product yields in an anionic metalloporphyrin-based artificial photosynthesis system

DOEpatents

Shelnutt, J.A.

1984-11-29

A method is disclosed improving product yields in an anionic metalloporphyrin-based artificial photosynthesis system for hydrogen generation. The method comprises forming an aqueous solution comprising an electron donor, methylviologen, and certain metalloporphyrins and metallochlorins, and irradiating said aqueous solution with light in the presence of a catalyst. In the photosynthesis process, solar energy is collected and stored in the form of a hydrogen. Ligands attached above and below the metalloporphyrin and metallochlorin plane are capable of sterically blocking photochemically inactive electrostatically bound ..pi..-..pi.. complexes which can develop.

Frustration and thermalization in an artificial magnetic quasicrystal

NASA Astrophysics Data System (ADS)

Shi, Dong; Budrikis, Zoe; Stein, Aaron; Morley, Sophie A.; Olmsted, Peter D.; Burnell, Gavin; Marrows, Christopher H.

2018-03-01

Artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional `skeleton' that spans the entire pattern and is capable of long-range order, surrounding `flippable' clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature.

Frustration and thermalization in an artificial magnetic quasicrystal

DOE PAGES

Shi, Dong; Budrikis, Zoe; Stein, Aaron; ...

2017-12-11

Here, artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional ‘skeleton’ that spans the entire pattern and is capablemore » of long-range order, surrounding ‘flippable’ clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature.« less

Frustration and thermalization in an artificial magnetic quasicrystal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Dong; Budrikis, Zoe; Stein, Aaron

Here, artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional ‘skeleton’ that spans the entire pattern and is capablemore » of long-range order, surrounding ‘flippable’ clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature.« less

A neural network based artificial vision system for licence plate recognition.

PubMed

Draghici, S

1997-02-01

This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.

Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

DTIC Science & Technology

2016-06-01

research is being done to incorporate the field of machine learning into intrusion detection. Machine learning is a branch of artificial intelligence (AI...adversarial drift." Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM. (2013) Kantarcioglu, M., Xi, B., and Clifton, C. "A...34 Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM. (2011) Dua, S., and Du, X. Data Mining and Machine Learning in

Automated Test Requirement Document Generation

DTIC Science & Technology

1987-11-01

DIAGNOSTICS BASED ON THE PRINCIPLES OF ARTIFICIAL INTELIGENCE ", 1984 International Test Conference, 01Oct84, (A3, 3, Cs D3, E2, G2, H2, 13, J6, K) 425...j0O GLOSSARY OF ACRONYMS 0 ABBREVIATION DEFINITION AFSATCOM Air Force Satellite Communication Al Artificial Intelligence ASIC Application Specific...In-Test Equipment (BITE) and AI ( Artificial Intelligence) - Expert Systems - need to be fully applied before a completely automated process can be

Extensions to the Parallel Real-Time Artificial Intelligence System (PRAIS) for fault-tolerant heterogeneous cycle-stealing reasoning

NASA Technical Reports Server (NTRS)

Goldstein, David

1991-01-01

Extensions to an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS) are discussed. PRAIS strives for transparently parallelizing production (rule-based) systems, even under real-time constraints. PRAIS accomplished these goals (presented at the first annual C Language Integrated Production System (CLIPS) conference) by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors. Results using the original PRAIS architecture over a network of Sun 3's, Sun 4's and VAX's are presented. Mechanisms using the producer-consumer model to extend the architecture for fault-tolerance and distributed truth maintenance initiation are also discussed.

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

PubMed

Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

PubMed Central

Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

Artificial intelligence costs, benefits, risks for selected spacecraft ground system automation scenarios

NASA Technical Reports Server (NTRS)

Truszkowski, Walter F.; Silverman, Barry G.; Kahn, Martha; Hexmoor, Henry

1988-01-01

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline), (2) standalone expert systems, (3) standardized, reusable knowledge base management systems (KBMS), and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Artificial intelligence costs, benefits, and risks for selected spacecraft ground system automation scenarios

NASA Technical Reports Server (NTRS)

Truszkowski, Walter F.; Silverman, Barry G.; Kahn, Martha; Hexmoor, Henry

1988-01-01

In response to a number of high-level strategy studies in the early 1980s, expert systems and artificial intelligence (AI/ES) efforts for spacecraft ground systems have proliferated in the past several years primarily as individual small to medium scale applications. It is useful to stop and assess the impact of this technology in view of lessons learned to date, and hopefully, to determine if the overall strategies of some of the earlier studies both are being followed and still seem relevant. To achieve that end four idealized ground system automation scenarios and their attendant AI architecture are postulated and benefits, risks, and lessons learned are examined and compared. These architectures encompass: (1) no AI (baseline); (2) standalone expert systems; (3) standardized, reusable knowledge base management systems (KBMS); and (4) a futuristic unattended automation scenario. The resulting artificial intelligence lessons learned, benefits, and risks for spacecraft ground system automation scenarios are described.

Optimization of knowledge-based systems and expert system building tools

NASA Technical Reports Server (NTRS)

Yasuda, Phyllis; Mckellar, Donald

1993-01-01

The objectives of the NASA-AMES Cooperative Agreement were to investigate, develop, and evaluate, via test cases, the system parameters and processing algorithms that constrain the overall performance of the Information Sciences Division's Artificial Intelligence Research Facility. Written reports covering various aspects of the grant were submitted to the co-investigators for the grant. Research studies concentrated on the field of artificial intelligence knowledge-based systems technology. Activities included the following areas: (1) AI training classes; (2) merging optical and digital processing; (3) science experiment remote coaching; (4) SSF data management system tests; (5) computer integrated documentation project; (6) conservation of design knowledge project; (7) project management calendar and reporting system; (8) automation and robotics technology assessment; (9) advanced computer architectures and operating systems; and (10) honors program.

PROUST: Knowledge-Based Program Understanding.

DTIC Science & Technology

1983-08-01

undeniably yes. If anything, PROUST is the minimum that is required! The basis for this conclusion is twofold: 1. In Artificial Intelligence research...Role of Plans in Intellegent Teaching Systems. In Brown, J. S. and Sleeman, D. (editors), Intellegent Tutoring Systems. New York. 1981. [8] Goldstein, I...95, 1978. (12] Rich, C. A Formal Representation for Plans in the Programmer’s Apprentice. In Proc. of the Seventh Int. Joint Conf. on Artificial

Design and Implementation of a Relational Database Management System for the AFIT Thesis Process.

DTIC Science & Technology

1985-09-01

AIRLIFT Gourdin 4. APPLIED MATHEMATICS Daneman Lee Na rga rsen ker 5. ARTIFICIAL INTELLEGENCE Gen et 6. CAPARILITY ASSESSMENT S Budde Talbott 31...05 ARTIFICIAL INTELLIGENCE 06 CAPABILITY ASSESSMENT 07 COMMUNIICATIONS 08 COMPUTER AIDED DESIGN 09 COMPUTER BASED TRAINING 10 COMPUTER SOFTWARE 11

Sensory motor systems of artificial and natural hands.

PubMed

Chappell, Paul H; Cranny, Andy; Cotton, Darryl P J; White, Neil M; Beeby, Steve P

2007-12-01

The surgeon Ambroise Paré designed an anthropomorphic hand for wounded soldiers in the 16th century. Since that time, there have been advances in technology through the use of computer-aided design, modern materials, electronic controllers and sensors to realise artificial hands which have good functionality and reliability. Data from touch, object slip, finger position and temperature sensors, mounted in the fingers and on the palm, can be used in feedback loops to automatically hold objects. A study of the natural neuromuscular systems reveals a complexity which can only in part be realised today with technology. Highlights of the parallels and differences between natural and artificial hands are discussed with reference to the Southampton Hand. The anatomical structure of parts of the natural systems can be made artificially such as the antagonist muscles using tendons. Theses solutions look promising as they are based on the natural form but in practice lack the desired physical specification. However, concepts of the lower spinal loops can be mimicked in principle. Some future devices will require greater skills from the surgeon to create the interface between the natural system and an artificial device. Such developments may offer a more natural control with ease of use for the limb deficient person.

A Survey of Artificial Immune System Based Intrusion Detection

PubMed Central

Li, Tao; Hu, Xinlei; Wang, Feng; Zou, Yang

2014-01-01

In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted. PMID:24790549

An advanced artificial intelligence tool for menu design.

PubMed

Khan, Abdus Salam; Hoffmann, Achim

2003-01-01

The computer-assisted menu design still remains a difficult task. Usually knowledge that aids in menu design by a computer is hard-coded and because of that a computerised menu planner cannot handle the menu design problem for an unanticipated client. To address this problem we developed a menu design tool, MIKAS (menu construction using incremental knowledge acquisition system), an artificial intelligence system that allows the incremental development of a knowledge-base for menu design. We allow an incremental knowledge acquisition process in which the expert is only required to provide hints to the system in the context of actual problem instances during menu design using menus stored in a so-called Case Base. Our system incorporates Case-Based Reasoning (CBR), an Artificial Intelligence (AI) technique developed to mimic human problem solving behaviour. Ripple Down Rules (RDR) are a proven technique for the acquisition of classification knowledge from expert directly while they are using the system, which complement CBR in a very fruitful way. This combination allows the incremental improvement of the menu design system while it is already in routine use. We believe MIKAS allows better dietary practice by leveraging a dietitian's skills and expertise. As such MIKAS has the potential to be helpful for any institution where dietary advice is practised.

Advanced training systems

NASA Technical Reports Server (NTRS)

Savely, Robert T.; Loftin, R. Bowen

1990-01-01

Training is a major endeavor in all modern societies. Common training methods include training manuals, formal classes, procedural computer programs, simulations, and on-the-job training. NASA's training approach has focussed primarily on on-the-job training in a simulation environment for both crew and ground based personnel. NASA must explore new approaches to training for the 1990's and beyond. Specific autonomous training systems are described which are based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground based support personnel that show an alternative to current training systems. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer Aided Training (ICAT) systems would provide much of the same experience that could be gained from the best on-the-job training.

Artificial emotion triggered stochastic behavior transitions with motivational gain effects for multi-objective robot tasks

NASA Astrophysics Data System (ADS)

Dağlarli, Evren; Temeltaş, Hakan

2007-04-01

This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.

VHBuild.com: A Web-Based System for Managing Knowledge in Projects.

ERIC Educational Resources Information Center

Li, Heng; Tang, Sandy; Man, K. F.; Love, Peter E. D.

2002-01-01

Describes an intelligent Web-based construction project management system called VHBuild.com which integrates project management, knowledge management, and artificial intelligence technologies. Highlights include an information flow model; time-cost optimization based on genetic algorithms; rule-based drawing interpretation; and a case-based…

Small Knowledge-Based Systems in Education and Training: Something New Under the Sun.

ERIC Educational Resources Information Center

Wilson, Brent G.; Welsh, Jack R.

1986-01-01

Discusses artificial intelligence, robotics, natural language processing, and expert or knowledge-based systems research; examines two large expert systems, MYCIN and XCON; and reviews the resources required to build large expert systems and affordable smaller systems (intelligent job aids) for training. Expert system vendors and products are…

Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country.

PubMed

Wu, Jia; Tan, Yanlin; Chen, Zhigang; Zhao, Ming

2018-06-01

Non-small cell lung cancer (NSCLC) is a high risk cancer and is usually scanned by PET-CT for testing, predicting and then give the treatment methods. However, in the actual hospital system, at least 640 images must be generated for each patient through PET-CT scanning. Especially in developing countries, a huge number of patients in NSCLC are attended by doctors. Artificial system can predict and make decision rapidly. According to explore and research artificial medical system, the selection of artificial observations also can result in low work efficiency for doctors. In this study, data information of 2,789,675 patients in three hospitals in China are collected, compiled, and used as the research basis; these data are obtained through image acquisition and diagnostic parameter machine decision-making method on the basis of the machine diagnosis and medical system design model of adjuvant therapy. By combining image and diagnostic parameters, the machine decision diagnosis auxiliary algorithm is established. Experimental result shows that the accuracy has reached 77% in NSCLC. Copyright © 2018 Elsevier B.V. All rights reserved.

Dual band metamaterial perfect absorber based on artificial dielectric "molecules".

PubMed

Liu, Xiaoming; Lan, Chuwen; Li, Bo; Zhao, Qian; Zhou, Ji

2016-07-13

Dual band metamaterial perfect absorbers with two absorption bands are highly desirable because of their potential application areas such as detectors, transceiver system, and spectroscopic imagers. However, most of these dual band metamaterial absorbers proposed were based on resonances of metal patterns. Here, we numerically and experimentally demonstrate a dual band metamaterial perfect absorber composed of artificial dielectric "molecules" with high symmetry. The artificial dielectric "molecule" consists of four "atoms" of two different sizes corresponding to two absorption bands with near unity absorptivity. Numerical and experimental absorptivity verify that the dual-band metamaterial absorber is polarization insensitive and can operate in wide-angle incidence.

Competent Systems: Effective, Efficient, Deliverable.

ERIC Educational Resources Information Center

Abramson, Bruce

Recent developments in artificial intelligence and decision analysis suggest reassessing the approaches commonly taken to the design of knowledge-based systems. Competent systems are based on models known as influence diagrams, which graphically capture a domain's basic objects and their interrelationships. Among the benefits offered by influence…

Artificial Intelligence in Maintenance: Proceedings of the Joint Services Workshop Held at Boulder, Colorado on 4-6 October 1983.

DTIC Science & Technology

1984-06-01

intelligence . I strongly suspect that we’ll use data links to Rome so we can take advantage of both of the computer systems. Again, we see the need for close... data base indexing system would come up with a hit on those three key words I’ve just said. What is it? The hit is "artificial intelligence ." (This...pieces of data and is not classificatory in nature. In MDX there is S an intelligent data base component, called PATREC [6, 7], for doing such reasoning

Supervised pixel classification using a feature space derived from an artificial visual system

NASA Technical Reports Server (NTRS)

Baxter, Lisa C.; Coggins, James M.

1991-01-01

Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

Predicting IVF Outcome: A Proposed Web-based System Using Artificial Intelligence.

PubMed

Siristatidis, Charalampos; Vogiatzi, Paraskevi; Pouliakis, Abraham; Trivella, Marialenna; Papantoniou, Nikolaos; Bettocchi, Stefano

2016-01-01

To propose a functional in vitro fertilization (IVF) prediction model to assist clinicians in tailoring personalized treatment of subfertile couples and improve assisted reproduction outcome. Construction and evaluation of an enhanced web-based system with a novel Artificial Neural Network (ANN) architecture and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and "trained" by a network expert in an IVF setting. The system is capable to act as a routine information technology platform for the IVF unit and is capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an artificial reproductive cycle. ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications. Copyright © 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

PubMed

Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn

2015-06-01

This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics. © The Author(s) 2013.

Automated Management Of Documents

NASA Technical Reports Server (NTRS)

Boy, Guy

1995-01-01

Report presents main technical issues involved in computer-integrated documentation. Problems associated with automation of management and maintenance of documents analyzed from perspectives of artificial intelligence and human factors. Technologies that may prove useful in computer-integrated documentation reviewed: these include conventional approaches to indexing and retrieval of information, use of hypertext, and knowledge-based artificial-intelligence systems.

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Elements of decisional dynamics: An agent-based approach applied to artificial financial market.

PubMed

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

The Hospital of the Future. Megatrends, Driving Forces, Barriers to Implementation, Overarching Perspectives, Major Trends into the Future, Implications for TATRC And Specific Recommendations for Action

DTIC Science & Technology

2008-10-01

Healthcare Systems Will Be Those That Work With Data/Info In New Ways • Artificial Intelligence Will Come to the Fore o Effectively Acquire...Education • Artificial Intelligence Will Assist in o History and Physical Examination o Imaging Selection via algorithms o Test Selection via algorithms...medical language into a simulation model based upon artificial intelligence , and • the content verification and validation of the cognitive

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

NASA Astrophysics Data System (ADS)

Janaki Sathya, D.; Geetha, K.

2017-12-01

Automatic mass or lesion classification systems are developed to aid in distinguishing between malignant and benign lesions present in the breast DCE-MR images, the systems need to improve both the sensitivity and specificity of DCE-MR image interpretation in order to be successful for clinical use. A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper. The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network. An optimal set of features based on the statistical textural features is presented. The investigational outcomes of the proposed suspicious lesion classifier algorithm therefore confirm that the resulting classifier performs better than other such classifiers reported in the literature. Therefore this classifier demonstrates that the improvement in both the sensitivity and specificity are possible through automated image analysis.

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

PubMed

Du, Tingsong; Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation

PubMed Central

Hu, Yang; Ke, Xianting

2015-01-01

An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713

Bio-based products from solar energy and carbon dioxide.

PubMed

Yu, Jian

2014-01-01

Producing bio-based products directly from CO₂ and solar energy is a desirable alternative to the conventional biorefining that relies on biomass feedstocks. The production paradigm is based on an artificial photosynthetic system that converts sunlight to electricity and H₂ via water electrolysis. An autotrophic H₂-oxidizing bacterium fixes CO₂ in dark conditions. The assimilated CO₂ is stored in bacterial cells as polyhydroxybutyrate (PHB), from which a range of products can be derived. Compared with natural photosynthesis of a fast-growing cyanobacterium, the artificial photosynthetic system has much higher energy efficiency and productivity of bio-based products. The new technology looks promising because of possible cost reduction in feedstock, equipment, and operation. Copyright © 2013 Elsevier Ltd. All rights reserved.

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

PubMed Central

Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

2016-01-01

In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved. PMID:27077868

An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

PubMed

Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

2016-04-12

In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

Building Better Decision-Support by Using Knowledge Discovery.

ERIC Educational Resources Information Center

Jurisica, Igor

2000-01-01

Discusses knowledge-based decision-support systems that use artificial intelligence approaches. Addresses the issue of how to create an effective case-based reasoning system for complex and evolving domains, focusing on automated methods for system optimization and domain knowledge evolution that can supplement knowledge acquired from domain…

Viruses, Artificial Viruses and Virus-Based Structures for Biomedical Applications.

PubMed

van Rijn, Patrick; Schirhagl, Romana

2016-06-01

Nanobiomaterials such as virus particles and artificial virus particles offer tremendous opportunities to develop new biomedical applications such as drug- or gene-delivery, imaging and sensing but also improve understanding of biological mechanisms. Recent advances within the field of virus-based systems give insights in how to mimic viral structures and virus assembly processes as well as understanding biodistribution, cell/tissue targeting, controlled and triggered disassembly or release and circulation times. All these factors are of high importance for virus-based functional systems. This review illustrates advances in mimicking and enhancing or controlling these aspects to a high degree toward delivery and imaging applications. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Generating Scenarios When Data Are Missing

NASA Technical Reports Server (NTRS)

Mackey, Ryan

2007-01-01

The Hypothetical Scenario Generator (HSG) is being developed in conjunction with other components of artificial-intelligence systems for automated diagnosis and prognosis of faults in spacecraft, aircraft, and other complex engineering systems. The HSG accepts, as input, possibly incomplete data on the current state of a system (see figure). The HSG models a potential fault scenario as an ordered disjunctive tree of conjunctive consequences, wherein the ordering is based upon the likelihood that a particular conjunctive path will be taken for the given set of inputs. The computation of likelihood is based partly on a numerical ranking of the degree of completeness of data with respect to satisfaction of the antecedent conditions of prognostic rules. The results from the HSG are then used by a model-based artificial- intelligence subsystem to predict realistic scenarios and states.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

A comparison of artificial turf.

PubMed

Naunheim, Rosanne; Parrott, Heather; Standeven, John

2004-12-01

In an attempt to decrease injuries, newer forms of artificial turf have been marketed. The purpose of this study was to determine whether a new shredded rubber-based turf improves impact attenuation. An instrumented computerized impact recording device (IRD, Techmark, Lansing, MI) was dropped 20 times from a height of 48 inches onto five types of turf used by a professional football team. Duncan's multiple range test shows that the new rubber-based field and the older foam field are not significantly different. There were significant differences, however, between sites on the shredded rubber-based field. The change from a foam-based system to a shredded rubber-based system had no effect on impact attenuation overall. However, areas in the shredded rubber-based field were significantly compacted, causing some sites to be much harder than the foam-based surface it replaced.

High-Frequency Dynamics Modulated by Collective Magnetization Reversal in Artificial Spin Ice

NASA Astrophysics Data System (ADS)

Jungfleisch, Matthias B.; Sklenar, Joseph; Ding, Junjia; Park, Jungsik; Pearson, John E.; Novosad, Valentine; Schiffer, Peter; Hoffmann, Axel

2017-12-01

Spin-torque ferromagnetic resonance arises in heavy metal-ferromagnet heterostructures when an alternating charge current is passed through the bilayer stack. The methodology to detect the resonance is based on the anisotropic magnetoresistance, which is the change in the electrical resistance due to different orientations of the magnetization. In connected networks of ferromagnetic nanowires, known as artificial spin ice, the magnetoresistance is rather complex owing to the underlying collective behavior of the geometrically frustrated magnetic domain structure. Here, we demonstrate spin-torque ferromagnetic resonance investigations in a square artificial spin-ice system and correlate our observations to magnetotransport measurements. The experimental findings are described using a simulation approach that highlights the importance of the correlated dynamics response of the magnetic system. Our results open the possibility of designing reconfigurable microwave oscillators and magnetoresistive devices based on connected networks of nanomagnets.

High-Frequency Dynamics Modulated by Collective Magnetization Reversal in Artificial Spin Ice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jungfleisch, Matthias B.; Sklenar, Joseph; Ding, Junjia

Spin-torque ferromagnetic resonance arises in heavy metal-ferromagnet heterostructures when an alternating charge current is passed through the bilayer stack. The methodology to detect the resonance is based on the anisotropic magnetoresistance, which is the change in the electrical resistance due to different orientations of the magnetization. In connected networks of ferromagnetic nanowires, known as artificial spin ice, the magnetoresistance is rather complex owing to the underlying collective behavior of the geometrically frustrated magnetic domain structure. Here, we demonstrate spin-torque ferromagnetic resonance investigations in a square artificial spin-ice system and correlate our observations to magneto-transport measurements. The experimental findings are describedmore » using a simulation approach that highlights the importance of the correlated dynamics response of the magnetic system. Our results open the possibility of designing reconfigurable microwave oscillators and magnetoresistive devices based on connected networks of nanomagnets.« less

Characterization of hybrid lighting systems of the Electrical Engineering Building in the Industrial University of Santander

NASA Astrophysics Data System (ADS)

Galvis, D.; Exposito, C.; Osma, G.; Amado, L.; Ordóñez, G.

2016-07-01

This paper presents an analysis of hybrid lighting systems of Electrical Engineering Building in the Industrial University of Santander, which is a pilot of green building for warm- tropical conditions. Analysis of lighting performance of inner spaces is based on lighting curves obtained from characterization of daylighting systems of these spaces. A computation tool was made in Excel-Visual Basic to simulate the behaviour of artificial lighting system considering artificial control system, user behaviour and solar condition. Also, this tool allows to estimate the electrical energy consumption of the lighting system for a day, a month and a year.

An Intelligent Active Video Surveillance System Based on the Integration of Virtual Neural Sensors and BDI Agents

NASA Astrophysics Data System (ADS)

Gregorio, Massimo De

In this paper we present an intelligent active video surveillance system currently adopted in two different application domains: railway tunnels and outdoor storage areas. The system takes advantages of the integration of Artificial Neural Networks (ANN) and symbolic Artificial Intelligence (AI). This hybrid system is formed by virtual neural sensors (implemented as WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from a computational and hardware point of view, and rather robust in performances. The system works on different scenarios and in difficult light conditions.

Exodus - Distributed artificial intelligence for Shuttle firing rooms

NASA Technical Reports Server (NTRS)

Heard, Astrid E.

1990-01-01

This paper describes the Expert System for Operations Distributed Users (EXODUS), a knowledge-based artificial intelligence system developed for the four Firing Rooms at the Kennedy Space Center. EXODUS is used by the Shuttle engineers and test conductors to monitor and control the sequence of tasks required for processing and launching Shuttle vehicles. In this paper, attention is given to the goals and the design of EXODUS, the operational requirements, and the extensibility of the technology.

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator

PubMed Central

Bouallègue, Soufiene; Garrido, Aitor J.; Haggège, Joseph

2018-01-01

Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances. PMID:29695127

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator.

PubMed

Ghefiri, Khaoula; Bouallègue, Soufiene; Garrido, Izaskun; Garrido, Aitor J; Haggège, Joseph

2018-04-24

Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

PubMed Central

Li, Zhining; Zhang, Yingtang; Yin, Gang

2018-01-01

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. PMID:29373544

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Artificial intelligence (AI) systems for interpreting complex medical datasets.

PubMed

Altman, R B

2017-05-01

Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability. © 2017 ASCPT.

Does Artificial Tutoring Foster Inquiry Based Learning?

ERIC Educational Resources Information Center

Schmoelz, Alexander; Swertz, Christian; Forstner, Alexandra; Barberi, Alessandro

2014-01-01

This contribution looks at the Intelligent Tutoring Interface for Technology Enhanced Learning, which integrates multistage-learning and inquiry-based learning in an adaptive e-learning system. Based on a common pedagogical ontology, adaptive e-learning systems can be enabled to recommend learning objects and activities, which follow inquiry-based…

Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

NASA Astrophysics Data System (ADS)

Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

2017-10-01

Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.

Learning Evolution and the Nature of Science Using Evolutionary Computing and Artificial Life

ERIC Educational Resources Information Center

Pennock, Robert T.

2007-01-01

Because evolution in natural systems happens so slowly, it is difficult to design inquiry-based labs where students can experiment and observe evolution in the way they can when studying other phenomena. New research in evolutionary computation and artificial life provides a solution to this problem. This paper describes a new A-Life software…

Developing Applications of Artificial Intelligence Technology To Provide Consultative Support in the Use of Research Methodology by Practitioners.

ERIC Educational Resources Information Center

Vitale, Michael R.; Romance, Nancy

Adopting perspectives based on applications of artificial intelligence proven in industry, this paper discusses methodological strategies and issues that underlie the development of such software environments. The general concept of an expert system is discussed in the context of its relevance to the problem of increasing the accessibility of…

Artificial photosynthetic antennas and reaction centers

DOE PAGES

Llansola-Portoles, Manuel J.; Gust, Devens; Moore, Thomas A.; ...

2017-03-01

Presently, the world is experiencing an unprecedented crisis associated with the CO2 produced by the use of fossil fuels to power our economies. As evidenced by the increasing levels in the atmosphere, the reduction of CO2 to biomass by photosynthesis cannot keep pace with production with the result that nature has lost control of the global carbon cycle. In order to restore control of the global carbon cycle to solar-driven processes, highly efficient artificial photosynthesis can augment photosynthesis in specific ways and places. The increased efficiency of artificial photosynthesis can provide both renewable carbon-based fuels and lower net atmospheric levelsmore » of CO2, which will preserve land and support the ecosystem services upon which all life on Earth depends. The development of artificial photosynthetic antennas and reaction centers contributes to the understanding of natural photosynthesis and to the knowledge base necessary for the development of future scalable technologies. This review focuses on the design and study of molecular and hybrid molecular-semiconductor nanoparticle based systems, all of which are inspired by functions found in photosynthesis and some of which are inspired by components of photosynthesis. In addition to constructs illustrating energy transfer, photoinduced electron transfer, charge shift reactions and proton coupled electron transfer, our review covers systems that produce proton motive force.« less

Artificial photosynthetic antennas and reaction centers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Llansola-Portoles, Manuel J.; Gust, Devens; Moore, Thomas A.

Presently, the world is experiencing an unprecedented crisis associated with the CO2 produced by the use of fossil fuels to power our economies. As evidenced by the increasing levels in the atmosphere, the reduction of CO2 to biomass by photosynthesis cannot keep pace with production with the result that nature has lost control of the global carbon cycle. In order to restore control of the global carbon cycle to solar-driven processes, highly efficient artificial photosynthesis can augment photosynthesis in specific ways and places. The increased efficiency of artificial photosynthesis can provide both renewable carbon-based fuels and lower net atmospheric levelsmore » of CO2, which will preserve land and support the ecosystem services upon which all life on Earth depends. The development of artificial photosynthetic antennas and reaction centers contributes to the understanding of natural photosynthesis and to the knowledge base necessary for the development of future scalable technologies. This review focuses on the design and study of molecular and hybrid molecular-semiconductor nanoparticle based systems, all of which are inspired by functions found in photosynthesis and some of which are inspired by components of photosynthesis. In addition to constructs illustrating energy transfer, photoinduced electron transfer, charge shift reactions and proton coupled electron transfer, our review covers systems that produce proton motive force.« less

Microoptical artificial compound eyes: from design to experimental verification of two different concepts

NASA Astrophysics Data System (ADS)

Duparré, Jacques; Wippermann, Frank; Dannberg, Peter; Schreiber, Peter; Bräuer, Andreas; Völkel, Reinhard; Scharf, Toralf

2005-09-01

Two novel objective types on the basis of artificial compound eyes are examined. Both imaging systems are well suited for fabrication using microoptics technology due to the small required lens sags. In the apposition optics a microlens array (MLA) and a photo detector array of different pitch in its focal plane are applied. The image reconstruction is based on moire magnification. Several generations of demonstrators of this objective type are manufactured by photo lithographic processes. This includes a system with opaque walls between adjacent channels and an objective which is directly applied onto a CMOS detector array. The cluster eye approach, which is based on a mixture of superposition compound eyes and the vision system of jumping spiders, produces a regular image. Here, three microlens arrays of different pitch form arrays of Keplerian microtelescopes with tilted optical axes, including a field lens. The microlens arrays of this demonstrator are also fabricated using microoptics technology, aperture arrays are applied. Subsequently the lens arrays are stacked to the overall microoptical system on wafer scale. Both fabricated types of artificial compound eye imaging systems are experimentally characterized with respect to resolution, sensitivity and cross talk between adjacent channels. Captured images are presented.

The coming of age of artificial intelligence in medicine.

PubMed

Patel, Vimla L; Shortliffe, Edward H; Stefanelli, Mario; Szolovits, Peter; Berthold, Michael R; Bellazzi, Riccardo; Abu-Hanna, Ameen

2009-05-01

This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its "adolescence" (Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the '90s? Artificial Intelligence in Medicine 1993;5:93-106). In this article, the discussants reflect on medical AI research during the subsequent years and characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision-making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems.

The Coming of Age of Artificial Intelligence in Medicine*

PubMed Central

Patel, Vimla L.; Shortliffe, Edward H.; Stefanelli, Mario; Szolovits, Peter; Berthold, Michael R.; Bellazzi, Riccardo; Abu-Hanna, Ameen

2009-01-01

Summary This paper is based on a panel discussion held at the Artificial Intelligence in Medicine Europe (AIME) conference in Amsterdam, The Netherlands, in July 2007. It had been more than 15 years since Edward Shortliffe gave a talk at AIME in which he characterized artificial intelligence (AI) in medicine as being in its “adolescence” (Shortliffe EH. The adolescence of AI in medicine: Will the field come of age in the ‘90s? Artificial Intelligence in Medicine 1993; 5:93–106). In this article, the discussants reflect on medical AI research during the subsequent years and attempt to characterize the maturity and influence that has been achieved to date. Participants focus on their personal areas of expertise, ranging from clinical decision making, reasoning under uncertainty, and knowledge representation to systems integration, translational bioinformatics, and cognitive issues in both the modeling of expertise and the creation of acceptable systems. PMID:18790621

Expert Systems in Reference Services.

ERIC Educational Resources Information Center

Roysdon, Christine, Ed.; White, Howard D., Ed.

1989-01-01

Eleven articles introduce expert systems applications in library and information science, and present design and implementation issues of system development for reference services. Topics covered include knowledge based systems, prototype development, the use of artificial intelligence to remedy current system inadequacies, and an expert system to…

Design of a portable artificial heart drive system based on efficiency analysis.

PubMed

Kitamura, T

1986-11-01

This paper discusses a computer simulation of a pneumatic portable piston-type artificial heart drive system with a linear d-c-motor. The purpose of the design is to obtain an artificial heart drive system with high efficiency and small dimensions to enhance portability. The design employs two factors contributing the total efficiency of the drive system. First, the dimensions of the pneumatic actuator were optimized under a cost function of the total efficiency. Second, the motor performance was studied in terms of efficiency. More than 50 percent of the input energy of the actuator with practical loads is consumed in the armature circuit in all linear d-c-motors with brushes. An optimal design is: the piston cross-sectional area of 10.5 cm2 cylinder longitudinal length of 10 cm. The total efficiency could be up to 25 percent by improving the gasket to reduce the frictional force.

Artificial intelligence and space power systems automation

NASA Technical Reports Server (NTRS)

Weeks, David J.

1987-01-01

Various applications of artificial intelligence to space electrical power systems are discussed. An overview is given of completed, on-going, and planned knowledge-based system activities. These applications include the Nickel-Cadmium Battery Expert System (NICBES) (the expert system interfaced with the Hubble Space Telescope electrical power system test bed); the early work with the Space Station Experiment Scheduler (SSES); the three expert systems under development in the space station advanced development effort in the core module power management and distribution system test bed; planned cooperation of expert systems in the Core Module Power Management and Distribution (CM/PMAD) system breadboard with expert systems for the space station at other research centers; and the intelligent data reduction expert system under development.

The application of artificial intelligence techniques to large distributed networks

NASA Technical Reports Server (NTRS)

Dubyah, R.; Smith, T. R.; Star, J. L.

1985-01-01

Data accessibility and transfer of information, including the land resources information system pilot, are structured as large computer information networks. These pilot efforts include the reduction of the difficulty to find and use data, reducing processing costs, and minimize incompatibility between data sources. Artificial Intelligence (AI) techniques were suggested to achieve these goals. The applicability of certain AI techniques are explored in the context of distributed problem solving systems and the pilot land data system (PLDS). The topics discussed include: PLDS and its data processing requirements, expert systems and PLDS, distributed problem solving systems, AI problem solving paradigms, query processing, and distributed data bases.

Information Processing in Cognition Process and New Artificial Intelligent Systems

NASA Astrophysics Data System (ADS)

Zheng, Nanning; Xue, Jianru

In this chapter, we discuss, in depth, visual information processing and a new artificial intelligent (AI) system that is based upon cognitive mechanisms. The relationship between a general model of intelligent systems and cognitive mechanisms is described, and in particular we explore visual information processing with selective attention. We also discuss a methodology for studying the new AI system and propose some important basic research issues that have emerged in the intersecting fields of cognitive science and information science. To this end, a new scheme for associative memory and a new architecture for an AI system with attractors of chaos are addressed.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

A review of European applications of artificial intelligence to space

NASA Technical Reports Server (NTRS)

Drummond, Mark (Editor); Stewart, Helen (Editor)

1993-01-01

The purpose is to describe the applications of Artificial Intelligence (AI) to the European Space program that are being developed or have been developed. The results of a study sponsored by the Artificial Intelligence Research and Development program of NASA's Office of Advanced Concepts and Technology (OACT) are described. The report is divided into two sections. The first consists of site reports, which are descriptions of the AI applications seen at each place visited. The second section consists of two summaries which synthesize the information in the site reports by organizing this information in two different ways. The first organizes the material in terms of the type of application, e.g., data analysis, planning and scheduling, and procedure management. The second organizes the material in terms of the component technologies of Artificial Intelligence which the applications used, e.g., knowledge based systems, model based reasoning, procedural reasoning, etc.

Open source hardware and software platform for robotics and artificial intelligence applications

NASA Astrophysics Data System (ADS)

Liang, S. Ng; Tan, K. O.; Lai Clement, T. H.; Ng, S. K.; Mohammed, A. H. Ali; Mailah, Musa; Azhar Yussof, Wan; Hamedon, Zamzuri; Yussof, Zulkifli

2016-02-01

Recent developments in open source hardware and software platforms (Android, Arduino, Linux, OpenCV etc.) have enabled rapid development of previously expensive and sophisticated system within a lower budget and flatter learning curves for developers. Using these platform, we designed and developed a Java-based 3D robotic simulation system, with graph database, which is integrated in online and offline modes with an Android-Arduino based rubbish picking remote control car. The combination of the open source hardware and software system created a flexible and expandable platform for further developments in the future, both in the software and hardware areas, in particular in combination with graph database for artificial intelligence, as well as more sophisticated hardware, such as legged or humanoid robots.

Powered Upper Limb Orthosis Actuation System Based on Pneumatic Artificial Muscles

NASA Astrophysics Data System (ADS)

Chakarov, Dimitar; Veneva, Ivanka; Tsveov, Mihail; Venev, Pavel

2018-03-01

The actuation system of a powered upper limb orthosis is studied in the work. To create natural safety in the mutual "man-robot" interaction, an actuation system based on pneumatic artificial muscles (PAM) is selected. Experimentally obtained force/contraction diagrams for bundles, consisting of different number of muscles are shown in the paper. The pooling force and the stiffness of the pneumatic actuators is assessed as a function of the number of muscles in the bundle and the supply pressure. Joint motion and torque is achieved by antagonistic actions through pulleys, driven by bundles of pneumatic muscles. Joint stiffness and joint torques are determined on condition of a power balance, as a function of the joint position, pressure, number of muscles and muscles

An imaging system based on laser optical feedback for fog vision applications

NASA Astrophysics Data System (ADS)

Belin, E.; Boucher, V.

2008-08-01

The Laboratoire Régional des Ponts et Chaussées d'Angers - LRPC of Angers is currently studying the feasability of applying an optical technique based on the principle of the laser optical feedback to long distance fog vision. Optical feedback set up allows the creation of images on roadsigns. To create artificial fog conditions we used a vibrating cell that produces a micro-spray of water according to the principle of acoustic cavitation. To scale the sensitivity of the system under duplicatible conditions we also used optical densities linked to first-sight visibility distances. The current system produces, in a few seconds, 200 × 200 pixel images of a roadsign seen through dense artificial fog.

Mission scheduling

NASA Technical Reports Server (NTRS)

Gaspin, Christine

1989-01-01

How a neural network can work, compared to a hybrid system based on an operations research and artificial intelligence approach, is investigated through a mission scheduling problem. The characteristic features of each system are discussed.

An artificial muscle model unit based on inorganic nanosheet sliding by photochemical reaction.

PubMed

Nabetani, Yu; Takamura, Hazuki; Hayasaka, Yuika; Sasamoto, Shin; Tanamura, Yoshihiko; Shimada, Tetsuya; Masui, Dai; Takagi, Shinsuke; Tachibana, Hiroshi; Tong, Zhiwei; Inoue, Haruo

2013-04-21

From the viewpoint of developing photoresponsive supramolecular systems in microenvironments to exhibit more sophisticated photo-functions even at the macroscopic level, inorganic/organic hybrid compounds based on clay or niobate nanosheets as the microenvironments were prepared, characterized, and examined for their photoreactions. We show here a novel type of artificial muscle model unit having much similarity with that in natural muscle fibrils. Upon photoirradiation, the organic/inorganic hybrid nanosheets reversibly slide horizontally on a giant scale, and the interlayer spaces in the layered hybrid structure shrink and expand vertically. In particular, our layered hybrid molecular system exhibits a macroscopic morphological change on a giant scale (~1500 nm) compared with the molecular size of ~1 nm, based on a reversible sliding mechanism.

Challenge Study: A Project-Based Learning on a Wireless Communication System at Technical High School

ERIC Educational Resources Information Center

Terasawa, Ikuo

2016-01-01

The challenge study is a project based learning curriculum at Technical High School aimed at the construction of a wireless communication system. The first period was engineering issues in the construction of an artificial satellite and the second period was a positional locating system based on the general purpose wire-less device--ZigBee device.…

[Development and application of a web-based expert system using artificial intelligence for management of mental health by Korean emigrants].

PubMed

Bae, Jeongyee

2013-04-01

The purpose of this project was to develop an international web-based expert system using principals of artificial intelligence and user-centered design for management of mental health by Korean emigrants. Using this system, anyone can access the system via computer access to the web. Our design process utilized principles of user-centered design with 4 phases: needs assessment, analysis, design/development/testing, and application release. A survey was done with 3,235 Korean emigrants. Focus group interviews were also conducted. Survey and analysis results guided the design of the web-based expert system. With this system, anyone can check their mental health status by themselves using a personal computer. The system analyzes facts based on answers to automated questions, and suggests solutions accordingly. A history tracking mechanism enables monitoring and future analysis. In addition, this system will include intervention programs to promote mental health status. This system is interactive and accessible to anyone in the world. It is expected that this management system will contribute to Korean emigrants' mental health promotion and allow researchers and professionals to share information on mental health.

Adding Learning to Knowledge-Based Systems: Taking the "Artificial" Out of AI

Treesearch

Daniel L. Schmoldt

1997-01-01

Both, knowledge-based systems (KBS) development and maintenance require time-consuming analysis of domain knowledge. Where example cases exist, KBS can be built, and later updated, by incorporating learning capabilities into their architecture. This applies to both supervised and unsupervised learning scenarios. In this paper, the important issues for learning systems-...

Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS).

PubMed

Latifoğlu, Fatma; Polat, Kemal; Kara, Sadik; Güneş, Salih

2008-02-01

In this study, we proposed a new medical diagnosis system based on principal component analysis (PCA), k-NN based weighting pre-processing, and Artificial Immune Recognition System (AIRS) for diagnosis of atherosclerosis from Carotid Artery Doppler Signals. The suggested system consists of four stages. First, in the feature extraction stage, we have obtained the features related with atherosclerosis disease using Fast Fourier Transformation (FFT) modeling and by calculating of maximum frequency envelope of sonograms. Second, in the dimensionality reduction stage, the 61 features of atherosclerosis disease have been reduced to 4 features using PCA. Third, in the pre-processing stage, we have weighted these 4 features using different values of k in a new weighting scheme based on k-NN based weighting pre-processing. Finally, in the classification stage, AIRS classifier has been used to classify subjects as healthy or having atherosclerosis. Hundred percent of classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in diagnosis of atherosclerosis disease.

Intelligent failure-tolerant control

NASA Technical Reports Server (NTRS)

Stengel, Robert F.

1991-01-01

An overview of failure-tolerant control is presented, beginning with robust control, progressing through parallel and analytical redundancy, and ending with rule-based systems and artificial neural networks. By design or implementation, failure-tolerant control systems are 'intelligent' systems. All failure-tolerant systems require some degrees of robustness to protect against catastrophic failure; failure tolerance often can be improved by adaptivity in decision-making and control, as well as by redundancy in measurement and actuation. Reliability, maintainability, and survivability can be enhanced by failure tolerance, although each objective poses different goals for control system design. Artificial intelligence concepts are helpful for integrating and codifying failure-tolerant control systems, not as alternatives but as adjuncts to conventional design methods.

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

PubMed Central

DeJournett, Leon; DeJournett, Jeremy

2016-01-01

Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)–based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. Method: We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient’s glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. Results: For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. Conclusions: This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting. PMID:27301982

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting.

PubMed

DeJournett, Leon; DeJournett, Jeremy

2016-11-01

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting. © 2016 Diabetes Technology Society.

A neural network approach to burst detection.

PubMed

Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

2002-01-01

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

Reasoning methods in medical consultation systems: artificial intelligence approaches.

PubMed

Shortliffe, E H

1984-01-01

It has been argued that the problem of medical diagnosis is fundamentally ill-structured, particularly during the early stages when the number of possible explanations for presenting complaints can be immense. This paper discusses the process of clinical hypothesis evocation, contrasts it with the structured decision making approaches used in traditional computer-based diagnostic systems, and briefly surveys the more open-ended reasoning methods that have been used in medical artificial intelligence (AI) programs. The additional complexity introduced when an advice system is designed to suggest management instead of (or in addition to) diagnosis is also emphasized. Example systems are discussed to illustrate the key concepts.

A Virtual Instrument System for Determining Sugar Degree of Honey

PubMed Central

Wu, Qijun; Gong, Xun

2015-01-01

This study established a LabVIEW-based virtual instrument system to measure optical activity through the communication of conventional optical instrument with computer via RS232 port. This system realized the functions for automatic acquisition, real-time display, data processing, results playback, and so forth. Therefore, it improved accuracy of the measurement results by avoiding the artificial operation, cumbersome data processing, and the artificial error in optical activity measurement. The system was applied to the analysis of the batch inspection on the sugar degree of honey. The results obtained were satisfying. Moreover, it showed advantages such as friendly man-machine dialogue, simple operation, and easily expanded functions. PMID:26504615

Research and applications: Artificial intelligence

NASA Technical Reports Server (NTRS)

Raphael, B.; Fikes, R. E.; Chaitin, L. J.; Hart, P. E.; Duda, R. O.; Nilsson, N. J.

1971-01-01

A program of research in the field of artificial intelligence is presented. The research areas discussed include automatic theorem proving, representations of real-world environments, problem-solving methods, the design of a programming system for problem-solving research, techniques for general scene analysis based upon television data, and the problems of assembling an integrated robot system. Major accomplishments include the development of a new problem-solving system that uses both formal logical inference and informal heuristic methods, the development of a method of automatic learning by generalization, and the design of the overall structure of a new complete robot system. Eight appendices to the report contain extensive technical details of the work described.

On Design and Implementation of Neural-Machine Interface for Artificial Legs

PubMed Central

Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

2011-01-01

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

Expert Systems: Tutors, Tools, and Tutees.

ERIC Educational Resources Information Center

Lippert, Renate C.

1989-01-01

Discusses the current status, research, and practical implications of artificial intelligence and expert systems in education. Topics discussed include computer-assisted instruction; intelligent computer-assisted instruction; intelligent tutoring systems; instructional strategies involving the creation of knowledge bases; decision aids;…

Optimization of groundwater artificial recharge systems using a genetic algorithm: a case study in Beijing, China

NASA Astrophysics Data System (ADS)

Hao, Qichen; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Huang, Linxian

2018-05-01

An optimization approach is used for the operation of groundwater artificial recharge systems in an alluvial fan in Beijing, China. The optimization model incorporates a transient groundwater flow model, which allows for simulation of the groundwater response to artificial recharge. The facilities' operation with regard to recharge rates is formulated as a nonlinear programming problem to maximize the volume of surface water recharged into the aquifers under specific constraints. This optimization problem is solved by the parallel genetic algorithm (PGA) based on OpenMP, which could substantially reduce the computation time. To solve the PGA with constraints, the multiplicative penalty method is applied. In addition, the facilities' locations are implicitly determined on the basis of the results of the recharge-rate optimizations. Two scenarios are optimized and the optimal results indicate that the amount of water recharged into the aquifers will increase without exceeding the upper limits of the groundwater levels. Optimal operation of this artificial recharge system can also contribute to the more effective recovery of the groundwater storage capacity.

Engineering Lipid Bilayer Membranes for Protein Studies

PubMed Central

Khan, Muhammad Shuja; Dosoky, Noura Sayed; Williams, John Dalton

2013-01-01

Lipid membranes regulate the flow of nutrients and communication signaling between cells and protect the sub-cellular structures. Recent attempts to fabricate artificial systems using nanostructures that mimic the physiological properties of natural lipid bilayer membranes (LBM) fused with transmembrane proteins have helped demonstrate the importance of temperature, pH, ionic strength, adsorption behavior, conformational reorientation and surface density in cellular membranes which all affect the incorporation of proteins on solid surfaces. Much of this work is performed on artificial templates made of polymer sponges or porous materials based on alumina, mica, and porous silicon (PSi) surfaces. For example, porous silicon materials have high biocompatibility, biodegradability, and photoluminescence, which allow them to be used both as a support structure for lipid bilayers or a template to measure the electrochemical functionality of living cells grown over the surface as in vivo. The variety of these media, coupled with the complex physiological conditions present in living systems, warrant a summary and prospectus detailing which artificial systems provide the most promise for different biological conditions. This study summarizes the use of electrochemical impedance spectroscopy (EIS) data on artificial biological membranes that are closely matched with previously published biological systems using both black lipid membrane and patch clamp techniques. PMID:24185908

A comparison between artificial and natural water oxidation.

PubMed

Li, Xichen; Chen, Guangju; Schinzel, Sandra; Siegbahn, Per E M

2011-11-14

Two artificial water oxidation catalysts, the blue dimer and the Llobet catalyst, have been studied using hybrid DFT methods. The results are compared to those for water oxidation in the natural photosystem II enzyme. Studies on the latter system have now reached a high level of understanding, at present much higher than the one for the artificial systems. A recent high resolution X-ray structural investigation of PSII has confirmed the main features of the structure of the oxygen evolving complex (OEC) suggested by previous DFT cluster studies. The O-O bond formation mechanism suggested is of direct coupling (DC) type between an oxygen radical and a bridging oxo ligand. A similar DC mechanism is found for the Llobet catalyst, while an acid-base (AB) mechanism is preferred for the blue dimer. All of them require at least one oxygen radical. Full energy diagrams, including both redox and chemical steps, have been constructed illustrating similarities and differences to the natural system. Unlike previous DFT studies, the results of the present study suggest that the blue dimer is rate-limited by the initial redox steps, and the Llobet catalyst by O(2) release. The results could be useful for further improvement of the artificial systems.

Artificial Intelligence Support for Computational Chemistry

NASA Astrophysics Data System (ADS)

Duch, Wlodzislaw

Possible forms of artificial intelligence (AI) support for quantum chemistry are discussed. Questions addressed include: what kind of support is desirable, what kind of support is feasible, what can we expect in the coming years. Advantages and disadvantages of current AI techniques are presented and it is argued that at present the memory-based systems are the most effective for large scale applications. Such systems may be used to predict the accuracy of calculations and to select the least expensive methods and basis sets belonging to the same accuracy class. Advantages of the Feature Space Mapping as an improvement on the memory based systems are outlined and some results obtained in classification problems given. Relevance of such classification systems to computational chemistry is illustrated with two examples showing similarity of results obtained by different methods that take electron correlation into account.

Integration of Artificial Photosynthesis System for Enhanced Electronic Energy-Transfer Efficacy: A Case Study for Solar-Energy Driven Bioconversion of Carbon Dioxide to Methanol.

PubMed

Ji, Xiaoyuan; Su, Zhiguo; Wang, Ping; Ma, Guanghui; Zhang, Songping

2016-09-01

Biocatalyzed artificial photosynthesis systems provide a promising strategy to store solar energy in a great variety of chemicals. However, the lack of direct interface between the light-capturing components and the oxidoreductase generally hinders the trafficking of the chemicals and photo-excited electrons into the active center of the redox biocatalysts. To address this problem, a completely integrated artificial photosynthesis system for enhanced electronic energy-transfer efficacy is reported by combining co-axial electrospinning/electrospray and layer-by-layer (LbL) self-assembly. The biocatalysis part including multiple oxidoreductases and coenzymes NAD(H) was in situ encapsulated inside the lumen polyelectrolyte-doped hollow nanofibers or microcapsules fabricated via co-axial electrospinning/electrospray; while the precise and spatial arrangement of the photocatalysis part, including electron mediator and photosensitizer for photo-regeneration of the coenzyme, was achieved by ion-exchange interaction-driven LbL self-assembly. The feasibility and advantages of this integrated artificial photosynthesis system is fully demonstrated by the catalyzed cascade reduction of CO2 to methanol by three dehydrogenases (formate, formaldehyde, and alcohol dehydrogenases), incorporating the photo-regeneration of NADH under visible-light irradiation. Compared to solution-based systems, the methanol yield increases from 35.6% to 90.6% using the integrated artificial photosynthesis. This work provides a novel platform for the efficient and sustained production of a broad range of chemicals and fuels from sunlight. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

NASA Astrophysics Data System (ADS)

Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

2017-01-01

People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

Comparison of LISP and MUMPS as implementation languages for knowledge-based systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Curtis, A.C.

1984-01-01

Major components of knowledge-based systems are summarized, along with the programming language features generally useful in their implementation. LISP and MUMPS are briefly described and compared as vehicles for building knowledge-based systems. The paper concludes with suggestions for extensions to MUMPS which might increase its usefulness in artificial intelligence applications without affecting the essential nature of the language. 8 references.

Artificial Intelligence for Pathologists Is Not Near--It Is Here: Description of a Prototype That Can Transform How We Practice Pathology Tomorrow.

PubMed

Ye, Jay J

2015-07-01

Pathologists' daily tasks consist of both the professional interpretation of slides and the secretarial tasks of translating these interpretations into final pathology reports, the latter of which is a time-consuming endeavor for most pathologists. To describe an artificial intelligence that performs secretarial tasks, designated as Secretary-Mimicking Artificial Intelligence (SMILE). The underling implementation of SMILE is a collection of computer programs that work in concert to "listen to" the voice commands and to "watch for" the changes of windows caused by slide bar code scanning; SMILE responds to these inputs by acting upon PowerPath Client windows (Sunquest Information Systems, Tucson, Arizona) and its Microsoft Word (Microsoft, Redmond, Washington) Add-In window, eventuating in the reports being typed and finalized. Secretary-Mimicking Artificial Intelligence also communicates relevant information to the pathologist via the computer speakers and message box on the screen. Secretary-Mimicking Artificial Intelligence performs many secretarial tasks intelligently and semiautonomously, with rapidity and consistency, thus enabling pathologists to focus on slide interpretation, which results in a marked increase in productivity, decrease in errors, and reduction of stress in daily practice. Secretary-Mimicking Artificial Intelligence undergoes encounter-based learning continually, resulting in a continuous improvement in its knowledge-based intelligence. Artificial intelligence for pathologists is both feasible and powerful. The future widespread use of artificial intelligence in our profession is certainly going to transform how we practice pathology.

Development of a linear induction motor based artificial muscle system.

PubMed

Gruber, A; Arguello, E; Silva, R

2013-01-01

We present the design of a linear induction motor based on electromagnetic interactions. The engine is capable of producing a linear movement from electricity. The design consists of stators arranged in parallel, which produce a magnetic field sufficient to displace a plunger along its axial axis. Furthermore, the winding has a shell and cap of ferromagnetic material that amplifies the magnetic field. This produces a force along the length of the motor that is similar to that of skeletal muscle. In principle, the objective is to use the engine in the development of an artificial muscle system for prosthetic applications, but it could have multiple applications, not only in the medical field, but in other industries.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

Home Camera-Based Fall Detection System for the Elderly.

PubMed

de Miguel, Koldo; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

2017-12-09

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.

Home Camera-Based Fall Detection System for the Elderly

PubMed Central

de Miguel, Koldo

2017-01-01

Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%. PMID:29232846

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

NASA Astrophysics Data System (ADS)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

The Potential of Computer-Based Expert Systems for Special Educators in Rural Settings.

ERIC Educational Resources Information Center

Parry, James D.; Ferrara, Joseph M.

Knowledge-based expert computer systems are addressing issues relevant to all special educators, but are particularly relevant in rural settings where human experts are less available because of distance and cost. An expert system is an application of artificial intelligence (AI) that typically engages the user in a dialogue resembling the…

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

A Text Knowledge Base from the AI Handbook.

ERIC Educational Resources Information Center

Simmons, Robert F.

1987-01-01

Describes a prototype natural language text knowledge system (TKS) that was used to organize 50 pages of a handbook on artificial intelligence as an inferential knowledge base with natural language query and command capabilities. Representation of text, database navigation, query systems, discourse structuring, and future research needs are…

Autonomous and Connected Vehicles: A Law Enforcement Primer

DTIC Science & Technology

2015-12-01

CYBERSECURITY FOR AUTOMOBILES Intelligent Transportation Systems (ITS) that are emerging around the globe achieve that classification based on the convergence...Car Works,” October 18, 2011, IEEE Spectrum, http://spectrum.ieee.org/automaton/robotics/ artificial - intelligence /how-google-self-driving-car-works...whereby artificial intelligence acts on behalf of a human, but carries the same life or death consequences.435 States should encourage and engage in

CBT Pilot Program Instructional Guide. Basic Drafting Skills Curriculum Delivered through CAD Workstations and Artificial Intelligence Software.

ERIC Educational Resources Information Center

Smith, Richard J.; Sauer, Mardelle A.

This guide is intended to assist teachers in using computer-aided design (CAD) workstations and artificial intelligence software to teach basic drafting skills. The guide outlines a 7-unit shell program that may also be used as a generic authoring system capable of supporting computer-based training (CBT) in other subject areas. The first section…

Distribution Planning: An Integration of Constraint Satisfaction & Heuristic Search Techniques

DTIC Science & Technology

1990-01-01

Proceedings of the Symposium on Aritificial Intelligence in ~~litary Logistics, Arlington, VA: American Defense Preparedness Assoc. pp. 177-182...dynamic changes, too many variables, and lack pf planning time. The Human Engineeri n ~ Laboratory (HEL) is developing artificial intelligence (AI...first attempt. The field of artificial intelligence includes a variety of knowledge-based approaches. Most widely known are Expert Systems, that are

Artificial transmembrane ion channels from self-assembling peptide nanotubes

NASA Astrophysics Data System (ADS)

Ghadiri, M. Reza; Granja, Juan R.; Buehler, Lukas K.

1994-05-01

NATURALLY occurring membrane channels and pores are formed from a large family of diverse proteins, peptides and organic secon-dary metabolites whose vital biological functions include control of ion flow, signal transduction, molecular transport and produc-tion of cellular toxins. But despite the availability of a large amount of biochemical information about these molecules1, the design and synthesis of artificial systems that can mimic the bio-logical function of natural compounds remains a formidable task2-12. Here we present a simple strategy for the design of artifi-cial membrane ion channels based on a self-assembled cylindrical β-sheet peptide architecture13. Our systems-essentially stacks of peptide rings-display good channel-mediated ion-transport activ-ity with rates exceeding 107 ions s-1, rivalling the performance of many naturally occurring counterparts. Such molecular assemblies should find use in the design of novel cytotoxic agents, membrane transport vehicles and drug-delivery systems.

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem

PubMed Central

Wang, Mingan; Li, Jianming; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators—cloning, mutation, and suppression—are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications—finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning—are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm. PMID:28630620

Cloud Model-Based Artificial Immune Network for Complex Optimization Problem.

PubMed

Wang, Mingan; Feng, Shuo; Li, Jianming; Li, Zhonghua; Xue, Yu; Guo, Dongliang

2017-01-01

This paper proposes an artificial immune network based on cloud model (AINet-CM) for complex function optimization problems. Three key immune operators-cloning, mutation, and suppression-are redesigned with the help of the cloud model. To be specific, an increasing half cloud-based cloning operator is used to adjust the dynamic clone multipliers of antibodies, an asymmetrical cloud-based mutation operator is used to control the adaptive evolution of antibodies, and a normal similarity cloud-based suppressor is used to keep the diversity of the antibody population. To quicken the searching convergence, a dynamic searching step length strategy is adopted. For comparative study, a series of numerical simulations are arranged between AINet-CM and the other three artificial immune systems, that is, opt-aiNet, IA-AIS, and AAIS-2S. Furthermore, two industrial applications-finite impulse response (FIR) filter design and proportional-integral-differential (PID) controller tuning-are investigated and the results demonstrate the potential searching capability and practical value of the proposed AINet-CM algorithm.

[Energy and memory efficient calculation of the accommodation demand in the artificial accommodation system].

PubMed

Nagel, J A; Beck, C; Harms, H; Stiller, P; Guth, H; Stachs, O; Bretthauer, G

2010-12-01

Presbyopia and cataract are gaining more and more importance in the ageing society. Both age-related complaints are accompanied with a loss of the eye's ability to accommodate. A new approach to restore accommodation is the Artificial Accommodation System, an autonomous micro system, which will be implanted into the capsular bag instead of a rigid intraocular lens. The Artificial Accommodation System will, depending on the actual demand for accommodation, autonomously adapt the refractive power of its integrated optical element. One possibility to measure the demand for accommodation non-intrusively is to analyse eye movements. We present an efficient algorithm, based on the CORDIC technique, to calculate the demand for accommodation from magnetic field sensor data. It can be shown that specialised algorithms significantly shorten calculation time without violating precision requirements. Additionally, a communication strategy for the wireless exchange of sensor data between the implants of the left and right eye is introduced. The strategy allows for a one-sided calculation of the demand for accommodation, resulting in an overall reduction of calculation time by 50 %. The presented methods enable autonomous microsystems, such as the Artificial Accommodation System, to save significant amounts of energy, leading to extended autonomous run-times. © Georg Thieme Verlag KG Stuttgart · New York.

BP artificial neural network based wave front correction for sensor-less free space optics communication

NASA Astrophysics Data System (ADS)

Li, Zhaokun; Zhao, Xiaohui

2017-02-01

The sensor-less adaptive optics (AO) is one of the most promising methods to compensate strong wave front disturbance in free space optics communication (FSO). The back propagation (BP) artificial neural network is applied for the sensor-less AO system to design a distortion correction scheme in this study. This method only needs one or a few online measurements to correct the wave front distortion compared with other model-based approaches, by which the real-time capacity of the system is enhanced and the Strehl Ratio (SR) is largely improved. Necessary comparisons in numerical simulation with other model-based and model-free correction methods proposed in Refs. [6,8,9,10] are given to show the validity and advantage of the proposed method.

Heart failure analysis dashboard for patient's remote monitoring combining multiple artificial intelligence technologies.

PubMed

Guidi, G; Pettenati, M C; Miniati, R; Iadanza, E

2012-01-01

In this paper we describe an Heart Failure analysis Dashboard that, combined with a handy device for the automatic acquisition of a set of patient's clinical parameters, allows to support telemonitoring functions. The Dashboard's intelligent core is a Computer Decision Support System designed to assist the clinical decision of non-specialist caring personnel, and it is based on three functional parts: Diagnosis, Prognosis, and Follow-up management. Four Artificial Intelligence-based techniques are compared for providing diagnosis function: a Neural Network, a Support Vector Machine, a Classification Tree and a Fuzzy Expert System whose rules are produced by a Genetic Algorithm. State of the art algorithms are used to support a score-based prognosis function. The patient's Follow-up is used to refine the diagnosis.

The prediction of the residual life of electromechanical equipment based on the artificial neural network

NASA Astrophysics Data System (ADS)

Zhukovskiy, Yu L.; Korolev, N. A.; Babanova, I. S.; Boikov, A. V.

2017-10-01

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

Artificial intelligent decision support for low-cost launch vehicle integrated mission operations

NASA Astrophysics Data System (ADS)

Szatkowski, Gerard P.; Schultz, Roger

1988-11-01

The feasibility, benefits, and risks associated with Artificial Intelligence (AI) Expert Systems applied to low cost space expendable launch vehicle systems are reviewed. This study is in support of the joint USAF/NASA effort to define the next generation of a heavy-lift Advanced Launch System (ALS) which will provide economical and routine access to space. The significant technical goals of the ALS program include: a 10 fold reduction in cost per pound to orbit, launch processing in under 3 weeks, and higher reliability and safety standards than current expendables. Knowledge-based system techniques are being explored for the purpose of automating decision support processes in onboard and ground systems for pre-launch checkout and in-flight operations. Issues such as: satisfying real-time requirements, providing safety validation, hardware and Data Base Management System (DBMS) interfacing, system synergistic effects, human interfaces, and ease of maintainability, have an effect on the viability of expert systems as a useful tool.

Artificial intelligent decision support for low-cost launch vehicle integrated mission operations

NASA Technical Reports Server (NTRS)

Szatkowski, Gerard P.; Schultz, Roger

1988-01-01

The feasibility, benefits, and risks associated with Artificial Intelligence (AI) Expert Systems applied to low cost space expendable launch vehicle systems are reviewed. This study is in support of the joint USAF/NASA effort to define the next generation of a heavy-lift Advanced Launch System (ALS) which will provide economical and routine access to space. The significant technical goals of the ALS program include: a 10 fold reduction in cost per pound to orbit, launch processing in under 3 weeks, and higher reliability and safety standards than current expendables. Knowledge-based system techniques are being explored for the purpose of automating decision support processes in onboard and ground systems for pre-launch checkout and in-flight operations. Issues such as: satisfying real-time requirements, providing safety validation, hardware and Data Base Management System (DBMS) interfacing, system synergistic effects, human interfaces, and ease of maintainability, have an effect on the viability of expert systems as a useful tool.

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network.

PubMed

Di Paola, Vieri; Marijuán, Pedro C; Lahoz-Beltra, Rafael

2004-01-01

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.

YADCLAN: yet another digitally-controlled linear artificial neuron.

PubMed

Frenger, Paul

2003-01-01

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

Application of Artificial Intelligence Techniques in Uninhabited Aerial Vehicle Flight

NASA Technical Reports Server (NTRS)

Dufrene, Warren R., Jr.

2004-01-01

This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA Southeastearn University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

Application of Artificial Intelligence Techniques in Uninhabitated Aerial Vehicle Flight

NASA Technical Reports Server (NTRS)

Dufrene, Warren R., Jr.

2003-01-01

This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA southeastern University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

The role of networks and artificial intelligence in nanotechnology design and analysis.

PubMed

Hudson, D L; Cohen, M E

2004-05-01

Techniques with their origins in artificial intelligence have had a great impact on many areas of biomedicine. Expert-based systems have been used to develop computer-assisted decision aids. Neural networks have been used extensively in disease classification and more recently in many bioinformatics applications including genomics and drug design. Network theory in general has proved useful in modeling all aspects of biomedicine from healthcare organizational structure to biochemical pathways. These methods show promise in applications involving nanotechnology both in the design phase and in interpretation of system functioning.

Expert Systems: An Overview for Teacher-Librarians.

ERIC Educational Resources Information Center

Orwig, Gary; Barron, Ann

1992-01-01

Provides an overview of expert systems for teacher librarians. Highlights include artificial intelligence and expert systems; the development of the MYCIN medical expert system; rule-based expert systems; the use of expert system shells to develop a specific system; and how to select an appropriate application for an expert system. (11 references)…

Quantum neural networks: Current status and prospects for development

NASA Astrophysics Data System (ADS)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis.

PubMed

Bekiari, Eleni; Kitsios, Konstantinos; Thabit, Hood; Tauschmann, Martin; Athanasiadou, Eleni; Karagiannis, Thomas; Haidich, Anna-Bettina; Hovorka, Roman; Tsapas, Apostolos

2018-04-18

To evaluate the efficacy and safety of artificial pancreas treatment in non-pregnant outpatients with type 1 diabetes. Systematic review and meta-analysis of randomised controlled trials. Medline, Embase, Cochrane Library, and grey literature up to 2 February 2018. Randomised controlled trials in non-pregnant outpatients with type 1 diabetes that compared the use of any artificial pancreas system with any type of insulin based treatment. Primary outcome was proportion (%) of time that sensor glucose level was within the near normoglycaemic range (3.9-10 mmol/L). Secondary outcomes included proportion (%) of time that sensor glucose level was above 10 mmol/L or below 3.9 mmol/L, low blood glucose index overnight, mean sensor glucose level, total daily insulin needs, and glycated haemoglobin. The Cochrane Collaboration risk of bias tool was used to assess study quality. 40 studies (1027 participants with data for 44 comparisons) were included in the meta-analysis. 35 comparisons assessed a single hormone artificial pancreas system, whereas nine comparisons assessed a dual hormone system. Only nine studies were at low risk of bias. Proportion of time in the near normoglycaemic range (3.9-10.0 mmol/L) was significantly higher with artificial pancreas use, both overnight (weighted mean difference 15.15%, 95% confidence interval 12.21% to 18.09%) and over a 24 hour period (9.62%, 7.54% to 11.7%). Artificial pancreas systems had a favourable effect on the proportion of time with sensor glucose level above 10 mmol/L (-8.52%, -11.14% to -5.9%) or below 3.9 mmol/L (-1.49%, -1.86% to -1.11%) over 24 hours, compared with control treatment. Robustness of findings for the primary outcome was verified in sensitivity analyses, by including only trials at low risk of bias (11.64%, 9.1% to 14.18%) or trials under unsupervised, normal living conditions (10.42%, 8.63% to 12.2%). Results were consistent in a subgroup analysis both for single hormone and dual hormone artificial pancreas systems. Artificial pancreas systems are an efficacious and safe approach for treating outpatients with type 1 diabetes. The main limitations of current research evidence on artificial pancreas systems are related to inconsistency in outcome reporting, small sample size, and short follow-up duration of individual trials. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A three-dimensional bioprinting system for use with a hydrogel-based biomaterial and printing parameter characterization.

PubMed

Song, Seung-Joon; Choi, Jaesoon; Park, Yong-Doo; Lee, Jung-Joo; Hong, So Young; Sun, Kyung

2010-11-01

Bioprinting is an emerging technology for constructing tissue or bioartificial organs with complex three-dimensional (3D) structures. It provides high-precision spatial shape forming ability on a larger scale than conventional tissue engineering methods, and simultaneous multiple components composition ability. Bioprinting utilizes a computer-controlled 3D printer mechanism for 3D biological structure construction. To implement minimal pattern width in a hydrogel-based bioprinting system, a study on printing characteristics was performed by varying printer control parameters. The experimental results showed that printing pattern width depends on associated printer control parameters such as printing flow rate, nozzle diameter, and nozzle velocity. The system under development showed acceptable feasibility of potential use for accurate printing pattern implementation in tissue engineering applications and is another example of novel techniques for regenerative medicine based on computer-aided biofabrication system. © 2010, Copyright the Authors. Artificial Organs © 2010, International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

1992-01-01

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

A New Multi-Agent Approach to Adaptive E-Education

NASA Astrophysics Data System (ADS)

Chen, Jing; Cheng, Peng

Improving customer satisfaction degree is important in e-Education. This paper describes a new approach to adaptive e-Education taking into account the full spectrum of Web service techniques and activities. It presents a multi-agents architecture based on artificial psychology techniques, which makes the e-Education process both adaptable and dynamic, and hence up-to-date. Knowledge base techniques are used to support the e-Education process, and artificial psychology techniques to deal with user psychology, which makes the e-Education system more effective and satisfying.

An auxiliary classification diagnosis software development of cervical cancer medical data based on various artificial neural networks

NASA Astrophysics Data System (ADS)

Qi, Yong; Lei, Kai; Zhang, Lizeqing; Xing, Ximing; Gou, Wenyue

2018-06-01

This paper introduced the development of a self-serving medical data assisted diagnosis software of cervical cancer on the basis of artificial neural network (SVN, FNN, KNN). The system is developed based on the idea of self-service platform, supported by the application and innovation of neural network algorithm in medical data identification. Furthermore, it combined the advanced methods in various fields to effectively solve the complicated and inaccurate problem of cervical canceration data in the traditional manual treatment.

Proceedings of the 1984 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1984-01-01

This conference contains papers on artificial intelligence, pattern recognition, and man-machine systems. Topics considered include concurrent minimization, a robot programming system, system modeling and simulation, camera calibration, thermal power plants, image processing, fault diagnosis, knowledge-based systems, power systems, hydroelectric power plants, expert systems, and electrical transients.

Materials for Diabetes Therapeutics

PubMed Central

Bratlie, Kaitlin M.; York, Roger L.; Invernale, Michael A.; Langer, Robert

2013-01-01

This review is focused on the materials and methods used to fabricate closed-loop systems for type 1 diabetes therapy. Herein, we give a brief overview of current methods used for patient care and discuss two types of possible treatments and the materials used for these therapies–(i) artificial pancreases, comprised of insulin producing cells embedded in a polymeric biomaterial, and (ii) totally synthetic pancreases formulated by integrating continuous glucose monitors with controlled insulin release through degradable polymers and glucose-responsive polymer systems. Both the artificial and the completely synthetic pancreas have two major design requirements: the device must be both biocompatible and be permeable to small molecules and proteins, such as insulin. Several polymers and fabrication methods of artificial pancreases are discussed: microencapsulation, conformal coatings, and planar sheets. We also review the two components of a completely synthetic pancreas. Several types of glucose sensing systems (including materials used for electrochemical, optical, and chemical sensing platforms) are discussed, in addition to various polymer-based release systems (including ethylene-vinyl acetate, polyanhydrides, and phenylboronic acid containing hydrogels). PMID:23184741

VWPS: A Ventilator Weaning Prediction System with Artificial Intelligence

NASA Astrophysics Data System (ADS)

Chen, Austin H.; Chen, Guan-Ting

How to wean patients efficiently off mechanical ventilation continues to be a challenge for medical professionals. In this paper we have described a novel approach to the study of a ventilator weaning prediction system (VWPS). Firstly, we have developed and written three Artificial Neural Network (ANN) algorithms to predict a weaning successful rate based on the clinical data. Secondly, we have implemented two user-friendly weaning success rate prediction systems; the VWPS system and the BWAP system. Both systems could be used to help doctors objectively and effectively predict whether weaning is appropriate for patients based on the patients' clinical data. Our system utilizes the powerful processing abilities of MatLab. Thirdly, we have calculated the performance through measures such as sensitivity and accuracy for these three algorithms. The results show a very high sensitivity (around 80%) and accuracy (around 70%). To our knowledge, this is the first design approach of its kind to be used in the study of ventilator weaning success rate prediction.

Local bounds preserving stabilization for continuous Galerkin discretization of hyperbolic systems

NASA Astrophysics Data System (ADS)

Mabuza, Sibusiso; Shadid, John N.; Kuzmin, Dmitri

2018-05-01

The objective of this paper is to present a local bounds preserving stabilized finite element scheme for hyperbolic systems on unstructured meshes based on continuous Galerkin (CG) discretization in space. A CG semi-discrete scheme with low order artificial dissipation that satisfies the local extremum diminishing (LED) condition for systems is used to discretize a system of conservation equations in space. The low order artificial diffusion is based on approximate Riemann solvers for hyperbolic conservation laws. In this case we consider both Rusanov and Roe artificial diffusion operators. In the Rusanov case, two designs are considered, a nodal based diffusion operator and a local projection stabilization operator. The result is a discretization that is LED and has first order convergence behavior. To achieve high resolution, limited antidiffusion is added back to the semi-discrete form where the limiter is constructed from a linearity preserving local projection stabilization operator. The procedure follows the algebraic flux correction procedure usually used in flux corrected transport algorithms. To further deal with phase errors (or terracing) common in FCT type methods, high order background dissipation is added to the antidiffusive correction. The resulting stabilized semi-discrete scheme can be discretized in time using a wide variety of time integrators. Numerical examples involving nonlinear scalar Burgers equation, and several shock hydrodynamics simulations for the Euler system are considered to demonstrate the performance of the method. For time discretization, Crank-Nicolson scheme and backward Euler scheme are utilized.

Adaptive power allocation schemes based on IAFS algorithm for OFDM-based cognitive radio systems

NASA Astrophysics Data System (ADS)

Zhang, Shuying; Zhao, Xiaohui; Liang, Cong; Ding, Xu

2017-01-01

In cognitive radio (CR) systems, reasonable power allocation can increase transmission rate of CR users or secondary users (SUs) as much as possible and at the same time insure normal communication among primary users (PUs). This study proposes an optimal power allocation scheme for the OFDM-based CR system with one SU influenced by multiple PU interference constraints. This scheme is based on an improved artificial fish swarm (IAFS) algorithm in combination with the advantage of conventional artificial fish swarm (ASF) algorithm and particle swarm optimisation (PSO) algorithm. In performance comparison of IAFS algorithm with other intelligent algorithms by simulations, the superiority of the IAFS algorithm is illustrated; this superiority results in better performance of our proposed scheme than that of the power allocation algorithms proposed by the previous studies in the same scenario. Furthermore, our proposed scheme can obtain higher transmission data rate under the multiple PU interference constraints and the total power constraint of SU than that of the other mentioned works.

Assessment of Passive Intestinal Permeability Using an Artificial Membrane Insert System.

PubMed

Berben, Philippe; Brouwers, Joachim; Augustijns, Patrick

2018-01-01

Despite reasonable predictive power of current cell-based and cell-free absorption models for the assessment of intestinal drug permeability, high costs and lengthy preparation steps hamper their use. The use of a simple artificial membrane (without any lipids present) as intestinal barrier substitute would overcome these hurdles. In the present study, a set of 14 poorly water-soluble drugs, dissolved in 2 different media (fasted state simulated/human intestinal fluids [FaSSIF/FaHIF]), were applied to the donor compartment of an artificial membrane insert system (AMI-system) containing a regenerated cellulose membrane. Furthermore, to investigate the predictive capacity of the AMI-system as substitute for the well-established Caco-2 system to assess intestinal permeability, the same set of 14 drugs dissolved in FaHIF were applied to the donor compartment of a Caco-2 system. For 14 drugs, covering a broad range of physicochemical parameters, a reasonable correlation between both absorption systems was observed, characterized by a Pearson correlation coefficient r of 0.95 (FaHIF). Using the AMI-system, an excellent predictive capacity of FaSSIF as surrogate medium for FaHIF was demonstrated (r = 0.96). Based on the acquired data, the AMI-system appears to be a time- and cost-effective tool for the early-stage estimation of passive intestinal permeability for poorly water-soluble drugs. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

PRAIS: Distributed, real-time knowledge-based systems made easy

NASA Technical Reports Server (NTRS)

Goldstein, David G.

1990-01-01

This paper discusses an architecture for real-time, distributed (parallel) knowledge-based systems called the Parallel Real-time Artificial Intelligence System (PRAIS). PRAIS strives for transparently parallelizing production (rule-based) systems, even when under real-time constraints. PRAIS accomplishes these goals by incorporating a dynamic task scheduler, operating system extensions for fact handling, and message-passing among multiple copies of CLIPS executing on a virtual blackboard. This distributed knowledge-based system tool uses the portability of CLIPS and common message-passing protocols to operate over a heterogeneous network of processors.

Artificial Intelligence In Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Vogel, Alison Andrews

1991-01-01

Paper compares four first-generation artificial-intelligence (Al) software systems for computational fluid dynamics. Includes: Expert Cooling Fan Design System (EXFAN), PAN AIR Knowledge System (PAKS), grid-adaptation program MITOSIS, and Expert Zonal Grid Generation (EZGrid). Focuses on knowledge-based ("expert") software systems. Analyzes intended tasks, kinds of knowledge possessed, magnitude of effort required to codify knowledge, how quickly constructed, performances, and return on investment. On basis of comparison, concludes Al most successful when applied to well-formulated problems solved by classifying or selecting preenumerated solutions. In contrast, application of Al to poorly understood or poorly formulated problems generally results in long development time and large investment of effort, with no guarantee of success.

A brief history and technical review of the expert system research

NASA Astrophysics Data System (ADS)

Tan, Haocheng

2017-09-01

The expert system is a computer system that emulates the decision-making ability of a human expert, which aims to solve complex problems by reasoning knowledge. It is an important branch of artificial intelligence. In this paper, firstly, we briefly introduce the development and basic structure of the expert system. Then, from the perspective of the enabling technology, we classify the current expert systems and elaborate four expert systems: The Rule-Based Expert System, the Framework-Based Expert System, the Fuzzy Logic-Based Expert System and the Expert System Based on Neural Network.

i-SAIRAS '90; Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Kobe, Japan, Nov. 18-20, 1990

NASA Technical Reports Server (NTRS)

1990-01-01

The present conference on artificial intelligence (AI), robotics, and automation in space encompasses robot systems, lunar and planetary robots, advanced processing, expert systems, knowledge bases, issues of operation and management, manipulator control, and on-orbit service. Specific issues addressed include fundamental research in AI at NASA, the FTS dexterous telerobot, a target-capture experiment by a free-flying robot, the NASA Planetary Rover Program, the Katydid system for compiling KEE applications to Ada, and speech recognition for robots. Also addressed are a knowledge base for real-time diagnosis, a pilot-in-the-loop simulation of an orbital docking maneuver, intelligent perturbation algorithms for space scheduling optimization, a fuzzy control method for a space manipulator system, hyperredundant manipulator applications, robotic servicing of EOS instruments, and a summary of astronaut inputs on automation and robotics for the Space Station Freedom.

Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

NASA Astrophysics Data System (ADS)

Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

2015-10-01

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

Artificial intelligence applied to process signal analysis

NASA Technical Reports Server (NTRS)

Corsberg, Dan

1988-01-01

Many space station processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect of the human/machine interface is the analysis and display of process information. Human operators can be overwhelmed by large clusters of alarms that inhibit their ability to diagnose and respond to a disturbance. Using artificial intelligence techniques and a knowledge base approach to this problem, the power of the computer can be used to filter and analyze plant sensor data. This will provide operators with a better description of the process state. Once a process state is recognized, automatic action could be initiated and proper system response monitored.

A fine resolution multifrequency polarimetric FM radar

NASA Technical Reports Server (NTRS)

Bredow, J.; Gogineni, S.; Leung, T.; Moore, R. K.

1988-01-01

A fine resolution polarimetric FM SAR was developed for optimization of polarimetric SARs and interpretation of SAR data via controlled experiments with surface-base sensors. The system is designed for collecting polarimetric data at 5.3 and 10 GHz over incidence angles from 0 to 60 deg. Features of the system include broad bandwidth to obtain fine range resolution, phase stabilization and linearization loop circuitry, and digital signal processing capability. The system is used in a research program to collect polarimetric backscatter data from artificial sea ice research and design trade-offs, laboratory and field evaluation, as well as results from experiments on artificial sea ice are presented.

Implementation of artificial intelligence rules in a data base management system

NASA Technical Reports Server (NTRS)

Feyock, S.

1986-01-01

The intelligent front end prototype was transformed into a RIM-integrated system. A RIM-based expert system was written which demonstrated the developed capability. The use of rules to produce extensibility of the intelligent front end, including the concept of demons and rule manipulation rules were investigated. Innovative approaches such as syntax programming were to be considered.

A knowledge-based decision support system for payload scheduling

NASA Technical Reports Server (NTRS)

Floyd, Stephen; Ford, Donnie

1988-01-01

The role that artificial intelligence/expert systems technologies play in the development and implementation of effective decision support systems is illustrated. A recently developed prototype system for supporting the scheduling of subsystems and payloads/experiments for NASA's Space Station program is presented and serves to highlight various concepts. The potential integration of knowledge based systems and decision support systems which has been proposed in several recent articles and presentations is illustrated.

An Application of Artificial Intelligence to the Implementation of Electronic Commerce

NASA Astrophysics Data System (ADS)

Srivastava, Anoop Kumar

In this paper, we present an application of Artificial Intelligence (AI) to the implementation of Electronic Commerce. We provide a multi autonomous agent based framework. Our agent based architecture leads to flexible design of a spectrum of multiagent system (MAS) by distributing computation and by providing a unified interface to data and programs. Autonomous agents are intelligent enough and provide autonomy, simplicity of communication, computation, and a well developed semantics. The steps of design and implementation are discussed in depth, structure of Electronic Marketplace, an ontology, the agent model, and interaction pattern between agents is given. We have developed mechanisms for coordination between agents using a language, which is called Virtual Enterprise Modeling Language (VEML). VEML is a integration of Java and Knowledge Query and Manipulation Language (KQML). VEML provides application programmers with potential to globally develop different kinds of MAS based on their requirements and applications. We have implemented a multi autonomous agent based system called VE System. We demonstrate efficacy of our system by discussing experimental results and its salient features.

Automatic labeling and characterization of objects using artificial neural networks

NASA Technical Reports Server (NTRS)

Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

1989-01-01

Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

Innovative applications of artificial intelligence

NASA Astrophysics Data System (ADS)

Schorr, Herbert; Rappaport, Alain

Papers concerning applications of artificial intelligence are presented, covering applications in aerospace technology, banking and finance, biotechnology, emergency services, law, media planning, music, the military, operations management, personnel management, retail packaging, and manufacturing assembly and design. Specific topics include Space Shuttle telemetry monitoring, an intelligent training system for Space Shuttle flight controllers, an expert system for the diagnostics of manufacturing equipment, a logistics management system, a cooling systems design assistant, and a knowledge-based integrated circuit design critic. Additional topics include a hydraulic circuit design assistant, the use of a connector assembly specification expert system to harness detailed assembly process knowledge, a mixed initiative approach to airlift planning, naval battle management decision aids, an inventory simulation tool, a peptide synthesis expert system, and a system for planning the discharging and loading of container ships.

Nondestructive testing of moisture separator reheater tubing system using Hall sensor array

NASA Astrophysics Data System (ADS)

Le, Minhhuy; Kim, Jungmin; Kim, Jisoo; Do, Hwa Sik; Lee, Jinyi

2018-01-01

This paper presents a nondestructive testing system for inspecting the moisture separator reheater (MSR) tubing system in a nuclear power plant. The technique is based on partial saturation eddy current testing in which a Hall sensor array is used to measure the radial component of the electromagnetic field distributed in the tubes. A finned MRS tube of ferritic stainless steel (SS439) with artificial, flat-bottom hole-type defects was used in the experiments. The results show that the proposed system has potential applications in the MSR system or ferromagnetic material tubes in general, which could detect the artificial defects of about 20% of the wall thickness (0.24 mm). Furthermore, the defect volume could be quantitatively evaluated.

COMPUTER SUPPORT SYSTEMS FOR ESTIMATING CHEMICAL TOXICITY: PRESENT CAPABILITIES AND FUTURE TRENDS

EPA Science Inventory

Computer Support Systems for Estimating Chemical Toxicity: Present Capabilities and Future Trends

A wide variety of computer-based artificial intelligence (AI) and decision support systems exist currently to aid in the assessment of toxicity for environmental chemicals. T...

A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems

PubMed Central

Merrick, Kathryn E.; Shafi, Kamran

2013-01-01

An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots. PMID:24198797

A game theoretic framework for incentive-based models of intrinsic motivation in artificial systems.

PubMed

Merrick, Kathryn E; Shafi, Kamran

2013-01-01

An emerging body of research is focusing on understanding and building artificial systems that can achieve open-ended development influenced by intrinsic motivations. In particular, research in robotics and machine learning is yielding systems and algorithms with increasing capacity for self-directed learning and autonomy. Traditional software architectures and algorithms are being augmented with intrinsic motivations to drive cumulative acquisition of knowledge and skills. Intrinsic motivations have recently been considered in reinforcement learning, active learning and supervised learning settings among others. This paper considers game theory as a novel setting for intrinsic motivation. A game theoretic framework for intrinsic motivation is formulated by introducing the concept of optimally motivating incentive as a lens through which players perceive a game. Transformations of four well-known mixed-motive games are presented to demonstrate the perceived games when players' optimally motivating incentive falls in three cases corresponding to strong power, affiliation and achievement motivation. We use agent-based simulations to demonstrate that players with different optimally motivating incentive act differently as a result of their altered perception of the game. We discuss the implications of these results both for modeling human behavior and for designing artificial agents or robots.

Artificial Intelligence and Information Retrieval.

ERIC Educational Resources Information Center

Teodorescu, Ioana

1987-01-01

Compares artificial intelligence and information retrieval paradigms for natural language understanding, reviews progress to date, and outlines the applicability of artificial intelligence to question answering systems. A list of principal artificial intelligence software for database front end systems is appended. (CLB)

Fluxonium-Based Artificial Molecule with a Tunable Magnetic Moment

NASA Astrophysics Data System (ADS)

Kou, A.; Smith, W. C.; Vool, U.; Brierley, R. T.; Meier, H.; Frunzio, L.; Girvin, S. M.; Glazman, L. I.; Devoret, M. H.

2017-07-01

Engineered quantum systems allow us to observe phenomena that are not easily accessible naturally. The LEGO®-like nature of superconducting circuits makes them particularly suited for building and coupling artificial atoms. Here, we introduce an artificial molecule, composed of two strongly coupled fluxonium atoms, which possesses a tunable magnetic moment. Using an applied external flux, one can tune the molecule between two regimes: one in which the ground-excited state manifold has a magnetic dipole moment and one in which the ground-excited state manifold has only a magnetic quadrupole moment. By varying the applied external flux, we find the coherence of the molecule to be limited by local flux noise. The ability to engineer and control artificial molecules paves the way for building more complex circuits for quantum simulation and protected qubits.

A framework for developing a mimetic tensor artificial viscosity for Lagrangian hydrocodes on arbitrary polygonal and polyhedral meshes (u)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lipnikov, Konstantin; Shashkov, Mikhail

2011-01-11

We construct a new mimetic tensor artificial viscosity on general polygonal and polyhedral meshes. The tensor artificial viscosity is based on a mimetic discretization of coordinate invariant operators, divergence of a tensor and gradient of a vector. The focus of this paper is on the symmetric form, div ({mu},{var_epsilon}(u)), of the tensor artificial viscosity where {var_epsilon}(u) is the symmetrized gradient of u and {mu}, is a tensor. The mimetic discretizations of this operator is derived for the case of a full tensor coefficient {mu}, that may reflect a shock direction. We demonstrate performance of the new viscosity for the Nohmore » implosion, Sedov explosion and Saltzman piston problems in both Cartesian and axisymmetric coordinate systems.« less

Validation of artificial skin equivalents as in vitro testing systems

NASA Astrophysics Data System (ADS)

Schmitt, Robert; Marx, Ulrich; Walles, Heike; Schober, Lena

2011-03-01

With the increasing complexity of the chemical composition of pharmaceuticals, cosmetics and everyday substances, the awareness of potential health issues and long term damages for humanoid organs is shifting into focus. Artificial in vitro testing systems play an important role in providing reliable test conditions and replacing precarious animal testing. Especially artificial skin equivalents ASEs are used for a broad spectrum of studies like penetration, irritation and corrosion of substances. One major challenge in tissue engineering is the qualification of each individual ASE as in vitro testing system. Due to biological fluctuations, the stratum corneum hornified layer of some ASEs may not fully develop or other defects might occur. For monitoring these effects we developed an fully automated Optical Coherence Tomography device. Here, we present different methods to characterize and evaluate the quality of the ASEs based on image and data processing of OCT B-scans. By analysing the surface structure, defects, like cuts or tears, are detectable. A further indicator for the quality of the ASE is the morphology of the tissue. This allows to determine if the skin model has reached the final growth state. We found, that OCT is a well suited technology for automatically characterizing artificial skin equivalents and validating the application as testing system.

System identification of smart structures using a wavelet neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors

NASA Astrophysics Data System (ADS)

Wu, Guodong; Feng, Ping; Wan, Xiang; Zhu, Liqiang; Shi, Yi; Wan, Qing

2016-03-01

Recent progress in using biomaterials to fabricate functional electronics has got growing attention for the new generation of environmentally friendly and biocompatible electronic devices. As a kind of biological material with rich source, proteins are essential natural component of all organisms. At the same time, artificial synaptic devices are of great significance for neuromorphic systems because they can emulate the signal process and memory behaviors of biological synapses. In this report, natural chicken albumen with high proton conductivity was used as the coupling electrolyte film for organic/inorganic hybrid synaptic devices fabrication. Some important synaptic functions including paired-pulse facilitation, dynamic filtering, short-term to long-term memory transition and spatial summation and shunting inhibition were successfully mimicked. Our results are very interesting for biological friendly artificial neuron networks and neuromorphic systems.

Artificial Immune Algorithm for Subtask Industrial Robot Scheduling in Cloud Manufacturing

NASA Astrophysics Data System (ADS)

Suma, T.; Murugesan, R.

2018-04-01

The current generation of manufacturing industry requires an intelligent scheduling model to achieve an effective utilization of distributed manufacturing resources, which motivated us to work on an Artificial Immune Algorithm for subtask robot scheduling in cloud manufacturing. This scheduling model enables a collaborative work between the industrial robots in different manufacturing centers. This paper discussed two optimizing objectives which includes minimizing the cost and load balance of industrial robots through scheduling. To solve these scheduling problems, we used the algorithm based on Artificial Immune system. The parameters are simulated with MATLAB and the results compared with the existing algorithms. The result shows better performance than existing.

Remote Agent Experiment

NASA Technical Reports Server (NTRS)

Benard, Doug; Dorais, Gregory A.; Gamble, Ed; Kanefsky, Bob; Kurien, James; Millar, William; Muscettola, Nicola; Nayak, Pandu; Rouquette, Nicolas; Rajan, Kanna;

Application and research of artificial water mist on photoelectric interference

NASA Astrophysics Data System (ADS)

He, Yuejun; Ren, Baolin

2018-04-01

Water mist is a new type of photoelectric interfering material. It can exert a strong interference and shielding effect on infrared light, laser and radar wave through scattering, reflection, refraction and absorption. Based on this, this paper illustrates the application of an artificial high pressure water mist technology in infrared interference system. First, the operating principle of the infrared interference system is introduced. Next, the design principle of self-excited rotary vortex nozzle, the key part of the system, is elaborated. Then, the calculation of the main control parameters of the system is clarified. In the end, the paper verifies interference and shielding effect of the system by experiment. Experiment shows that the interference system can significantly reduce infrared signature of the target, featuring excellent infrared interference performance and high practical value.

A neutron spectrum unfolding computer code based on artificial neural networks

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2014-02-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in the HTML format. NSDann unfolding code is freely available, upon request to the authors.

Bioartificial liver: current status.

PubMed

Pless, G; Sauer, I M

2005-11-01

Liver failure remains a life-threatening syndrome. With the growing disparity between the number of suitable donor organs and the number of patients awaiting transplantation, efforts have been made to optimize the allocation of organs, to find alternatives to cadaveric liver transplantation, and to develop extracorporeal methods to support or replace the function of the failing organ. An extracorporeal liver support system has to provide the main functions of the liver: detoxification, synthesis, and regulation. The understanding that the critical issue of the clinical syndrome in liver failure is the accumulation of toxins not cleared by the failing liver led to the development of artificial filtration and adsorption devices (artificial liver support). Based on this hypothesis, the removal of lipophilic, albumin-bound substances, such as bilirubin, bile acids, metabolites of aromatic amino acids, medium-chain fatty acids, and cytokines, should be beneficial to the clinical course of a patient in liver failure. Artificial detoxification devices currently under clinical evaluation include the Molecular Adsorbent Recirculating System (MARS), Single-Pass Albumin Dialysis (SPAD), and the Prometheus system. The complex tasks of regulation and synthesis remain to be addressed by the use of liver cells (bioartificial liver support). The Extracorporeal Liver Assist Device (ELAD), HepatAssist, Modular Extracorporeal Liver Support system (MELS), and the Amsterdam Medical Center Bioartificial Liver (AMC-BAL) are bioartificial systems. This article gives a brief overview on these artificial and bioartificial devices and discusses remaining obstacles.

Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques.

PubMed

Ferreira, F J O; Crispim, V R; Silva, A X

2010-06-01

In this study the development of a methodology to detect illicit drugs and plastic explosives is described with the objective of being applied in the realm of public security. For this end, non-destructive assay with neutrons was used and the technique applied was the real time neutron radiography together with computerized tomography. The system is endowed with automatic responses based upon the application of an artificial intelligence technique. In previous tests using real samples, the system proved capable of identifying 97% of the inspected materials. Copyright 2010 Elsevier Ltd. All rights reserved.

Active vision in satellite scene analysis

NASA Technical Reports Server (NTRS)

Naillon, Martine

1994-01-01

In earth observation or planetary exploration it is necessary to have more and, more autonomous systems, able to adapt to unpredictable situations. This imposes the use, in artificial systems, of new concepts in cognition, based on the fact that perception should not be separated from recognition and decision making levels. This means that low level signal processing (perception level) should interact with symbolic and high level processing (decision level). This paper is going to describe the new concept of active vision, implemented in Distributed Artificial Intelligence by Dassault Aviation following a 'structuralist' principle. An application to spatial image interpretation is given, oriented toward flexible robotics.

Artificial neural network does better spatiotemporal compressive sampling

NASA Astrophysics Data System (ADS)

Lee, Soo-Young; Hsu, Charles; Szu, Harold

2012-06-01

Spatiotemporal sparseness is generated naturally by human visual system based on artificial neural network modeling of associative memory. Sparseness means nothing more and nothing less than the compressive sensing achieves merely the information concentration. To concentrate the information, one uses the spatial correlation or spatial FFT or DWT or the best of all adaptive wavelet transform (cf. NUS, Shen Shawei). However, higher dimensional spatiotemporal information concentration, the mathematics can not do as flexible as a living human sensory system. The reason is obviously for survival reasons. The rest of the story is given in the paper.

Report on Ada (Trademark) Program Libraries Workshop Held at Monterey, California on November 1-3, 1983,

DTIC Science & Technology

1983-11-03

capability. An intelligent library management system will be supported by knowledge-based techniques. In fact, until a formal specification of library...from artificial intelligence and information science 2 might also be useful, for example automatic indexing and cataloging schemes, methods for fast...Artificial Intelligence 5:1045-1058, 1977. [Burstall & Goguen 801 Burstall, R. M., and Goguen, J. A. The Semantics of Clear, a Specification Language. In

Design and simulation of a cable-pulley-based transmission for artificial ankle joints

NASA Astrophysics Data System (ADS)

Liu, Huaxin; Ceccarelli, Marco; Huang, Qiang

2016-06-01

In this paper, a mechanical transmission based on cable pulley is proposed for human-like actuation in the artificial ankle joints of human-scale. The anatomy articular characteristics of the human ankle is discussed for proper biomimetic inspiration in designing an accurate, efficient, and robust motion control of artificial ankle joint devices. The design procedure is presented through the inclusion of conceptual considerations and design details for an interactive solution of the transmission system. A mechanical design is elaborated for the ankle joint angular with pitch motion. A multi-body dynamic simulation model is elaborated accordingly and evaluated numerically in the ADAMS environment. Results of the numerical simulations are discussed to evaluate the dynamic performance of the proposed design solution and to investigate the feasibility of the proposed design in future applications for humanoid robots.

Knowledge-Based Systems Research

DTIC Science & Technology

1990-08-24

P. S., Laird, J. E., Newell, A. and McCarl, R. 1991. A Preliminary Analysis of the SOAR Architecture as a Basis for General Intelligence . Artifcial ...on reverse of neceSSjr’y gnd identify by block nhmber) FIELD I GRO’= SUB-C.OROUC Artificial Intelligence , Blackboard Systems, U°nstraint Satisfaction...knowledge acquisition; symbolic simulation; logic-based systems with self-awareness; SOAR, an architecture for general intelligence and learning

Clips as a knowledge based language

NASA Technical Reports Server (NTRS)

Harrington, James B.

1987-01-01

CLIPS is a language for writing expert systems applications on a personal or small computer. Here, the CLIPS programming language is described and compared to three other artificial intelligence (AI) languages (LISP, Prolog, and OPS5) with regard to the processing they provide for the implementation of a knowledge based system (KBS). A discussion is given on how CLIPS would be used in a control system.

Social energy: mining energy from the society

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jun Jason; Gao, David Wenzhong; Zhang, Yingchen

The inherent nature of energy, i.e., physicality, sociality and informatization, implies the inevitable and intensive interaction between energy systems and social systems. From this perspective, we define 'social energy' as a complex sociotechnical system of energy systems, social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions. The recent advancement in intelligent technology, including artificial intelligence and machine learning technologies, sensing and communication in Internet of Things technologies, and massive high performance computing and extreme-scale data analytics technologies, enables the possibility of substantial advancement in socio-technical system optimization, scheduling, control and management. In thismore » paper, we provide a discussion on the nature of energy, and then propose the concept and intention of social energy systems for electrical power. A general methodology of establishing and investigating social energy is proposed, which is based on the ACP approach, i.e., 'artificial systems' (A), 'computational experiments' (C) and 'parallel execution' (P), and parallel system methodology. A case study on the University of Denver (DU) campus grid is provided and studied to demonstrate the social energy concept. In the concluding remarks, we discuss the technical pathway, in both social and nature sciences, to social energy, and our vision on its future.« less

Artificial Immune System for Recognizing Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2005-01-01

A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.

Knowledge Based Artificial Augmentation Intelligence Technology: Next Step in Academic Instructional Tools for Distance Learning

ERIC Educational Resources Information Center

Crowe, Dale; LaPierre, Martin; Kebritchi, Mansureh

2017-01-01

With augmented intelligence/knowledge based system (KBS) it is now possible to develop distance learning applications to support both curriculum and administrative tasks. Instructional designers and information technology (IT) professionals are now moving from the programmable systems era that started in the 1950s to the cognitive computing era.…

Advanced microprocessor based power protection system using artificial neural network techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Z.; Kalam, A.; Zayegh, A.

This paper describes an intelligent embedded microprocessor based system for fault classification in power system protection system using advanced 32-bit microprocessor technology. The paper demonstrates the development of protective relay to provide overcurrent protection schemes for fault detection. It also describes a method for power fault classification in three-phase system based on the use of neural network technology. The proposed design is implemented and tested on a single line three phase power system in power laboratory. Both the hardware and software development are described in detail.

A formal approach to validation and verification for knowledge-based control systems

NASA Technical Reports Server (NTRS)

Castore, Glen

1987-01-01

As control systems become more complex in response to desires for greater system flexibility, performance and reliability, the promise is held out that artificial intelligence might provide the means for building such systems. An obstacle to the use of symbolic processing constructs in this domain is the need for verification and validation (V and V) of the systems. Techniques currently in use do not seem appropriate for knowledge-based software. An outline of a formal approach to V and V for knowledge-based control systems is presented.

Artificial Intelligence in Autonomous Telescopes

NASA Astrophysics Data System (ADS)

Mahoney, William; Thanjavur, Karun

2011-03-01

Artificial Intelligence (AI) is key to the natural evolution of today's automated telescopes to fully autonomous systems. Based on its rapid development over the past five decades, AI offers numerous, well-tested techniques for knowledge based decision making essential for real-time telescope monitoring and control, with minimal - and eventually no - human intervention. We present three applications of AI developed at CFHT for monitoring instantaneous sky conditions, assessing quality of imaging data, and a prototype for scheduling observations in real-time. Closely complementing the current remote operations at CFHT, we foresee further development of these methods and full integration in the near future.

Artificial intelligence technology assessment for the US Army Depot System Command

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pennock, K A

1991-07-01

This assessment of artificial intelligence (AI) has been prepared for the US Army's Depot System Command (DESCOM) by Pacific Northwest Laboratory. The report describes several of the more promising AI technologies, focusing primarily on knowledge-based systems because they have been more successful in commercial applications than any other AI technique. The report also identifies potential Depot applications in the areas of procedural support, scheduling and planning, automated inspection, training, diagnostics, and robotic systems. One of the principal objectives of the report is to help decisionmakers within DESCOM to evaluate AI as a possible tool for solving individual depot problems. Themore » report identifies a number of factors that should be considered in such evaluations. 22 refs.« less

[The application and development of artificial intelligence in medical diagnosis systems].

PubMed

Chen, Zhencheng; Jiang, Yong; Xu, Mingyu; Wang, Hongyan; Jiang, Dazong

2002-09-01

This paper has reviewed the development of artificial intelligence in medical practice and medical diagnostic expert systems, and has summarized the application of artificial neural network. It explains that a source of difficulty in medical diagnostic system is the co-existence of multiple diseases--the potentially inter-related diseases. However, the difficulty of image expert systems is inherent in high-level vision. And it increases the complexity of expert system in medical image. At last, the prospect for the development of artificial intelligence in medical image expert systems is made.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network

PubMed Central

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-01-01

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition. PMID:27527175

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

PubMed

Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

2016-08-04

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

The complementarity of the technical tools of tissue engineering and the concepts of artificial organs for the design of functional bioartificial tissues.

PubMed

Lenas, Petros; Moreno, Angel; Ikonomou, Laertis; Mayer, Joerg; Honda, Hiroyuki; Novellino, Antonio; Pizarro, Camilo; Nicodemou-Lena, Eleni; Rodergas, Silvia; Pintor, Jesus

2008-09-01

Although tissue engineering uses powerful biological tools, it still has a weak conceptual foundation, which is restricted at the cell level. The design criteria at the cell level are not directly related with the tissue functions, and consequently, such functions cannot be implemented in bioartificial tissues with the currently used methods. On the contrary, the field of artificial organs focuses on the function of the artificial organs that are treated in the design as integral entities, instead of the optimization of the artificial organ components. The field of artificial organs has already developed and tested methodologies that are based on system concepts and mathematical-computational methods that connect the component properties with the desired global organ function. Such methodologies are needed in tissue engineering for the design of bioartificial tissues with tissue functions. Under the framework of biomedical engineering, artificial organs and tissue engineering do not present competitive approaches, but are rather complementary and should therefore design a common future for the benefit of patients.

Fabrication and Properties of Composite Artificial Muscles Based on Nylon and a Shape Memory Alloy

NASA Astrophysics Data System (ADS)

Yin, Haibin; Zhou, Jia; Li, Junfeng; Joseph, Vincent S.

2018-05-01

This paper focuses on the design, fabrication and investigation of the mechanical properties of new artificial muscles formed by twisting and annealing. The artificial muscles designed by twisting nylon have become a popular topic in the field of smart materials due to their high mechanical performance with a large deformation and power density. However, the complexity of the heating and cooling system required to control the nylon muscle is a disadvantage, so we have proposed a composite artificial muscle for providing a direct electricity-driven actuation by integrating nylon and a shape memory alloy (SMA). In this paper, the design and fabrication process of these composite artificial muscles are introduced before their mechanical properties, which include the deformation, stiffness, load and response, are investigated. The results show that these composite artificial muscles that integrate nylon and a SMA provide better mechanical properties and yield up to a 44.1% deformation and 3.43 N driving forces. The good performance and direct electro-thermal actuation make these composite muscles ideal for driving robots in a method similar to human muscles.

[Artificial intelligence in sleep analysis (ARTISANA)--modelling visual processes in sleep classification].

PubMed

Schwaibold, M; Schöller, B; Penzel, T; Bolz, A

2001-05-01

We describe a novel approach to the problem of automated sleep stage recognition. The ARTISANA algorithm mimics the behaviour of a human expert visually scoring sleep stages (Rechtschaffen and Kales classification). It comprises a number of interacting components that imitate the stepwise approach of the human expert, and artificial intelligence components. On the basis of parameters extracted at 1-s intervals from the signal curves, artificial neural networks recognize the incidence of typical patterns, e.g. delta activity or K complexes. This is followed by a rule interpretation stage that identifies the sleep stage with the aid of a neuro-fuzzy system while taking account of the context. Validation studies based on the records of 8 patients with obstructive sleep apnoea have confirmed the potential of this approach. Further features of the system include the transparency of the decision-taking process, and the flexibility of the option for expanding the system to cover new patterns and criteria.

Predicting asthma exacerbations using artificial intelligence.

PubMed

Finkelstein, Joseph; Wood, Jeffrey

2013-01-01

Modern telemonitoring systems identify a serious patient deterioration when it already occurred. It would be much more beneficial if the upcoming clinical deterioration were identified ahead of time even before a patient actually experiences it. The goal of this study was to assess artificial intelligence approaches which potentially can be used in telemonitoring systems for advance prediction of changes in disease severity before they actually occur. The study dataset was based on daily self-reports submitted by 26 adult asthma patients during home telemonitoring consisting of 7001 records. Two classification algorithms were employed for building predictive models: naïve Bayesian classifier and support vector machines. Using a 7-day window, a support vector machine was able to predict asthma exacerbation to occur on the day 8 with the accuracy of 0.80, sensitivity of 0.84 and specificity of 0.80. Our study showed that methods of artificial intelligence have significant potential in developing individualized decision support for chronic disease telemonitoring systems.

AITSO: A Tool for Spatial Optimization Based on Artificial Immune Systems

PubMed Central

Zhao, Xiang; Liu, Yaolin; Liu, Dianfeng; Ma, Xiaoya

2015-01-01

A great challenge facing geocomputation and spatial analysis is spatial optimization, given that it involves various high-dimensional, nonlinear, and complicated relationships. Many efforts have been made with regard to this specific issue, and the strong ability of artificial immune system algorithms has been proven in previous studies. However, user-friendly professional software is still unavailable, which is a great impediment to the popularity of artificial immune systems. This paper describes a free, universal tool, named AITSO, which is capable of solving various optimization problems. It provides a series of standard application programming interfaces (APIs) which can (1) assist researchers in the development of their own problem-specific application plugins to solve practical problems and (2) allow the implementation of some advanced immune operators into the platform to improve the performance of an algorithm. As an integrated, flexible, and convenient tool, AITSO contributes to knowledge sharing and practical problem solving. It is therefore believed that it will advance the development and popularity of spatial optimization in geocomputation and spatial analysis. PMID:25678911

Synthetic biology with artificially expanded genetic information systems. From personalized medicine to extraterrestrial life.

PubMed

Benner, Steven A; Hutter, Daniel; Sismour, A Michael

2003-01-01

Over 15 years ago, the Benner group noticed that the DNA alphabet need not be limited to the four standard nucleotides known in natural DNA. Rather, twelve nucleobases forming six base pairs joined by mutually exclusive hydrogen bonding patterns are possible within the geometry of the Watson-Crick pair (Fig. 1). Synthesis and studies on these compounds have brought us to the threshold of a synthetic biology, an artificial chemical system that does basic processes needed for life (in particular, Darwinian evolution), but with unnatural chemical structures. At the same time, the artificial genetic information systems (AEGIS) that we have developed have been used in FDA-approved commercial tests for managing HIV and hepatitis C infections in individual patients, and in a tool that seeks the virus for severe acute respiratory syndrome (SARS). AEGIS also supports the next generation of robotic probes to search for genetic molecules on Mars, Europa, and elsewhere where NASA probes will travel.

Simulation of a Schema Theory-Based Knowledge Delivery System for Scientists.

ERIC Educational Resources Information Center

Vaughan, W. S., Jr.; Mavor, Anne S.

A future, automated, interactive, knowledge delivery system for use by researchers was tested using a manual cognitive model. Conceptualized from schema/frame/script theories in cognitive psychology and artificial intelligence, this hypothetical system was simulated by two psychologists who interacted with four researchers in microbiology to…

Educational Assessment Using Intelligent Systems. Research Report. ETS RR-08-68

ERIC Educational Resources Information Center

Shute, Valerie J.; Zapata-Rivera, Diego

2008-01-01

Recent advances in educational assessment, cognitive science, and artificial intelligence have made it possible to integrate valid assessment and instruction in the form of modern computer-based intelligent systems. These intelligent systems leverage assessment information that is gathered from various sources (e.g., summative and formative). This…

Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots

DTIC Science & Technology

2010-09-24

system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based

Application of Adaptive Decision Aiding Systems to Computer-Assisted Instruction. Final Report, January-December 1974.

ERIC Educational Resources Information Center

May, Donald M.; And Others

The minicomputer-based Computerized Diagnostic and Decision Training (CDDT) system described combines the principles of artificial intelligence, decision theory, and adaptive computer assisted instruction for training in electronic troubleshooting. The system incorporates an adaptive computer program which learns the student's diagnostic and…

The application of intelligent process control to space based systems

NASA Technical Reports Server (NTRS)

Wakefield, G. Steve

1990-01-01

The application of Artificial Intelligence to electronic and process control can help attain the autonomy and safety requirements of manned space systems. An overview of documented applications within various industries is presented. The development process is discussed along with associated issues for implementing an intelligence process control system.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

PubMed

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Solar fuels via artificial photosynthesis.

PubMed

Gust, Devens; Moore, Thomas A; Moore, Ana L

2009-12-21

Because sunlight is diffuse and intermittent, substantial use of solar energy to meet humanity's needs will probably require energy storage in dense, transportable media via chemical bonds. Practical, cost effective technologies for conversion of sunlight directly into useful fuels do not currently exist, and will require new basic science. Photosynthesis provides a blueprint for solar energy storage in fuels. Indeed, all of the fossil-fuel-based energy consumed today derives from sunlight harvested by photosynthetic organisms. Artificial photosynthesis research applies the fundamental scientific principles of the natural process to the design of solar energy conversion systems. These constructs use different materials, and researchers tune them to produce energy efficiently and in forms useful to humans. Fuel production via natural or artificial photosynthesis requires three main components. First, antenna/reaction center complexes absorb sunlight and convert the excitation energy to electrochemical energy (redox equivalents). Then, a water oxidation complex uses this redox potential to catalyze conversion of water to hydrogen ions, electrons stored as reducing equivalents, and oxygen. A second catalytic system uses the reducing equivalents to make fuels such as carbohydrates, lipids, or hydrogen gas. In this Account, we review a few general approaches to artificial photosynthetic fuel production that may be useful for eventually overcoming the energy problem. A variety of research groups have prepared artificial reaction center molecules. These systems contain a chromophore, such as a porphyrin, covalently linked to one or more electron acceptors, such as fullerenes or quinones, and secondary electron donors. Following the excitation of the chromophore, photoinduced electron transfer generates a primary charge-separated state. Electron transfer chains spatially separate the redox equivalents and reduce electronic coupling, slowing recombination of the charge-separated state to the point that catalysts can use the stored energy for fuel production. Antenna systems, employing a variety of chromophores that absorb light throughout the visible spectrum, have been coupled to artificial reaction centers and have incorporated control and photoprotective processes borrowed from photosynthesis. Thus far, researchers have not discovered practical solar-driven catalysts for water oxidation and fuel production that are robust and use earth-abundant elements, but they have developed artificial systems that use sunlight to produce fuel in the laboratory. For example, artificial reaction centers, where electrons are injected from a dye molecule into the conduction band of nanoparticulate titanium dioxide on a transparent electrode, coupled to catalysts, such as platinum or hydrogenase enzymes, can produce hydrogen gas. Oxidizing equivalents from such reaction centers can be coupled to iridium oxide nanoparticles, which can oxidize water. This system uses sunlight to split water to oxygen and hydrogen fuel, but efficiencies are low and an external electrical potential is required. Although attempts at artificial photosynthesis fall short of the efficiencies necessary for practical application, they illustrate that solar fuel production inspired by natural photosynthesis is achievable in the laboratory. More research will be needed to identify the most promising artificial photosynthetic systems and realize their potential.

Energy-efficient lighting system for television

DOEpatents

Cawthorne, Duane C.

1987-07-21

A light control system for a television camera comprises an artificial light control system which is cooperative with an iris control system. This artificial light control system adjusts the power to lamps illuminating the camera viewing area to provide only sufficient artificial illumination necessary to provide a sufficient video signal when the camera iris is substantially open.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

NASA Astrophysics Data System (ADS)

Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

Control of an automated mobile manipulator using artificial immune system

NASA Astrophysics Data System (ADS)

Deepak, B. B. V. L.; Parhi, Dayal R.

2016-03-01

This paper addresses the coordination and control of a wheeled mobile manipulator (WMM) using artificial immune system. The aim of the developed methodology is to navigate the system autonomously and transport jobs and tools in manufacturing environments. This study integrates the kinematic structures of a four-axis manipulator and a differential wheeled mobile platform. The motion of the developed WMM is controlled by the complete system of parametric equation in terms of joint velocities and makes the robot to follow desired trajectories by the manipulator and platform within its workspace. The developed robot system performs its action intelligently according to the sensed environmental criteria within its search space. To verify the effectiveness of the proposed immune-based motion planner for WMM, simulations as well as experimental results are presented in various unknown environments.

Teaching artificial neural systems to drive: Manual training techniques for autonomous systems

NASA Technical Reports Server (NTRS)

Shepanski, J. F.; Macy, S. A.

1987-01-01

A methodology was developed for manually training autonomous control systems based on artificial neural systems (ANS). In applications where the rule set governing an expert's decisions is difficult to formulate, ANS can be used to extract rules by associating the information an expert receives with the actions taken. Properly constructed networks imitate rules of behavior that permits them to function autonomously when they are trained on the spanning set of possible situations. This training can be provided manually, either under the direct supervision of a system trainer, or indirectly using a background mode where the networks assimilates training data as the expert performs its day-to-day tasks. To demonstrate these methods, an ANS network was trained to drive a vehicle through simulated freeway traffic.

Multiprocessor Neural Network in Healthcare.

PubMed

Godó, Zoltán Attila; Kiss, Gábor; Kocsis, Dénes

2015-01-01

A possible way of creating a multiprocessor artificial neural network is by the use of microcontrollers. The RISC processors' high performance and the large number of I/O ports mean they are greatly suitable for creating such a system. During our research, we wanted to see if it is possible to efficiently create interaction between the artifical neural network and the natural nervous system. To achieve as much analogy to the living nervous system as possible, we created a frequency-modulated analog connection between the units. Our system is connected to the living nervous system through 128 microelectrodes. Two-way communication is provided through A/D transformation, which is even capable of testing psychopharmacons. The microcontroller-based analog artificial neural network can play a great role in medical singal processing, such as ECG, EEG etc.

New application of intelligent agents in sporadic amyotrophic lateral sclerosis identifies unexpected specific genetic background.

PubMed

Penco, Silvana; Buscema, Massimo; Patrosso, Maria Cristina; Marocchi, Alessandro; Grossi, Enzo

2008-05-30

Few genetic factors predisposing to the sporadic form of amyotrophic lateral sclerosis (ALS) have been identified, but the pathology itself seems to be a true multifactorial disease in which complex interactions between environmental and genetic susceptibility factors take place. The purpose of this study was to approach genetic data with an innovative statistical method such as artificial neural networks to identify a possible genetic background predisposing to the disease. A DNA multiarray panel was applied to genotype more than 60 polymorphisms within 35 genes selected from pathways of lipid and homocysteine metabolism, regulation of blood pressure, coagulation, inflammation, cellular adhesion and matrix integrity, in 54 sporadic ALS patients and 208 controls. Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis Advanced intelligent systems based on novel coupling of artificial neural networks and evolutionary algorithms have been applied. The results obtained have been compared with those derived from the use of standard neural networks and classical statistical analysis. An unexpected discovery of a strong genetic background in sporadic ALS using a DNA multiarray panel and analytical processing of the data with advanced artificial neural networks was found. The predictive accuracy obtained with Linear Discriminant Analysis and Standard Artificial Neural Networks ranged from 70% to 79% (average 75.31%) and from 69.1 to 86.2% (average 76.6%) respectively. The corresponding value obtained with Advanced Intelligent Systems reached an average of 96.0% (range 94.4 to 97.6%). This latter approach allowed the identification of seven genetic variants essential to differentiate cases from controls: apolipoprotein E arg158cys; hepatic lipase -480 C/T; endothelial nitric oxide synthase 690 C/T and glu298asp; vitamin K-dependent coagulation factor seven arg353glu, glycoprotein Ia/IIa 873 G/A and E-selectin ser128arg. This study provides an alternative and reliable method to approach complex diseases. Indeed, the application of a novel artificial intelligence-based method offers a new insight into genetic markers of sporadic ALS pointing out the existence of a strong genetic background.

The future of the artificial kidney: moving towards wearable and miniaturized devices.

PubMed

Ronco, C; Davenport, A; Gura, V

2011-01-01

New directions in dialysis research include cheaper treatments, home based therapies and simpler methods of blood purification. These objectives may be probably obtained with innovations in the field of artificial kidney through the utilization of new disciplines such as miniaturization, microfluidics, nanotechnology. This research may lead to a new era of dialysis in which the new challenges are transportability, wearability and why not the possibility to develop implantable devices. Although we are not there yet, a new series of papers have recently been published disclosing interesting and promising results on the application of wearable ultrafiltration systems (WUF) and wearable artificial kidneys (WAK). Some of them use extracorporeal blood cleansing as a method of blood purification while others use peritoneal dialysis as a treatment modality (ViWAK and AWAK.) A special mention deserves the wearable/portable ultrafiltration system for the therapy of overhydration and congestive heart failure (WAKMAN). This system will allow dehospitalization and treatment of patients with less comorbidity and improved tolerance. On the way to the wearable artificial kidney, new discoveries have been made such as a complete system for hemofiltration in newborns (CARPEDIEM). The neonate in fact is the typical patient who may benefit from miniaturization of the dialysis circuit. This review analyzes the rationale for such endeavour and the challenges to overcome in order to make possible a true ambulatory dialysis treatment. Some initial results with these new devices are presented. We would like to stimulate a collaborative effort to make a quantum leap in technology making the wearable artificial kidney a reality rather than a dream. 

Artificial Intelligent Platform as Decision Tool for Asset Management, Operations and Maintenance.

PubMed

2018-01-04

An Artificial Intelligence (AI) system has been developed and implemented for water, wastewater and reuse plants to improve management of sensors, short and long term maintenance plans, asset and investment management plans. It is based on an integrated approach to capture data from different computer systems and files. It adds a layer of intelligence to the data. It serves as a repository of key current and future operations and maintenance conditions that a plant needs have knowledge of. With this information, it is able to simulate the configuration of processes and assets for those conditions to improve or optimize operations, maintenance and asset management, using the IViewOps (Intelligent View of Operations) model. Based on the optimization through model runs, it is able to create output files that can feed data to other systems and inform the staff regarding optimal solutions to the conditions experienced or anticipated in the future.

Based on Artificial Neural Network to Realize K-Parameter Analysis of Vehicle Air Spring System

NASA Astrophysics Data System (ADS)

Hung, San-Shan; Hsu, Chia-Ning; Hwang, Chang-Chou; Chen, Wen-Jan

2017-10-01

In recent years, because of the air-spring control technique is more mature, that air- spring suspension systems already can be used to replace the classical vehicle suspension system. Depend on internal pressure variation of the air-spring, thestiffnessand the damping factor can be adjusted. Because of air-spring has highly nonlinear characteristic, therefore it isn’t easy to construct the classical controller to control the air-spring effectively. The paper based on Artificial Neural Network to propose a feasible control strategy. By using offline way for the neural network design and learning to the air-spring in different initial pressures and different loads, offline method through, predict air-spring stiffness parameter to establish a model. Finally, through adjusting air-spring internal pressure to change the K-parameter of the air-spring, realize the well dynamic control performance of air-spring suspension.

Object-oriented model-driven control

NASA Technical Reports Server (NTRS)

Drysdale, A.; Mcroberts, M.; Sager, J.; Wheeler, R.

1994-01-01

A monitoring and control subsystem architecture has been developed that capitalizes on the use of modeldriven monitoring and predictive control, knowledge-based data representation, and artificial reasoning in an operator support mode. We have developed an object-oriented model of a Controlled Ecological Life Support System (CELSS). The model based on the NASA Kennedy Space Center CELSS breadboard data, tracks carbon, hydrogen, and oxygen, carbodioxide, and water. It estimates and tracks resorce-related parameters such as mass, energy, and manpower measurements such as growing area required for balance. We are developing an interface with the breadboard systems that is compatible with artificial reasoning. Initial work is being done on use of expert systems and user interface development. This paper presents an approach to defining universally applicable CELSS monitor and control issues, and implementing appropriate monitor and control capability for a particular instance: the KSC CELSS Breadboard Facility.

Synthetic Approach to biomolecular science by cyborg supramolecular chemistry.

PubMed

Kurihara, Kensuke; Matsuo, Muneyuki; Yamaguchi, Takumi; Sato, Sota

2018-02-01

To imitate the essence of living systems via synthetic chemistry approaches has been attempted. With the progress in supramolecular chemistry, it has become possible to synthesize molecules of a size and complexity close to those of biomacromolecules. Recently, the combination of precisely designed supramolecules with biomolecules has generated structural platforms for designing and creating unique molecular systems. Bridging between synthetic chemistry and biomolecular science is also developing methodologies for the creation of artificial cellular systems. This paper provides an overview of the recently expanding interdisciplinary research to fuse artificial molecules with biomolecules, that can deepen our understanding of the dynamical ordering of biomolecules. Using bottom-up approaches based on the precise chemical design, synthesis and hybridization of artificial molecules with biological materials have been realizing the construction of sophisticated platforms having the fundamental functions of living systems. The effective hybrid, molecular cyborg, approaches enable not only the establishment of dynamic systems mimicking nature and thus well-defined models for biophysical understanding, but also the creation of those with highly advanced, integrated functions. This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato. Copyright © 2017 Elsevier B.V. All rights reserved.

[Review of wireless energy transmission system for total artificial heart].

PubMed

Zhang, Chi; Yang, Ming

2009-11-01

This paper sums up the fundamental structure of wireless energy transmission system for total artificial heart, and compares the key parameters and performance of some representative systems. After that, it is discussed that the future development trend of wireless energy transmission system for total artificial heart.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Development of a Laparoscopic Box Trainer Based on Open Source Hardware and Artificial Intelligence for Objective Assessment of Surgical Psychomotor Skills.

PubMed

Alonso-Silverio, Gustavo A; Pérez-Escamirosa, Fernando; Bruno-Sanchez, Raúl; Ortiz-Simon, José L; Muñoz-Guerrero, Roberto; Minor-Martinez, Arturo; Alarcón-Paredes, Antonio

2018-05-01

A trainer for online laparoscopic surgical skills assessment based on the performance of experts and nonexperts is presented. The system uses computer vision, augmented reality, and artificial intelligence algorithms, implemented into a Raspberry Pi board with Python programming language. Two training tasks were evaluated by the laparoscopic system: transferring and pattern cutting. Computer vision libraries were used to obtain the number of transferred points and simulated pattern cutting trace by means of tracking of the laparoscopic instrument. An artificial neural network (ANN) was trained to learn from experts and nonexperts' behavior for pattern cutting task, whereas the assessment of transferring task was performed using a preestablished threshold. Four expert surgeons in laparoscopic surgery, from hospital "Raymundo Abarca Alarcón," constituted the experienced class for the ANN. Sixteen trainees (10 medical students and 6 residents) without laparoscopic surgical skills and limited experience in minimal invasive techniques from School of Medicine at Universidad Autónoma de Guerrero constituted the nonexperienced class. Data from participants performing 5 daily repetitions for each task during 5 days were used to build the ANN. The participants tend to improve their learning curve and dexterity with this laparoscopic training system. The classifier shows mean accuracy and receiver operating characteristic curve of 90.98% and 0.93, respectively. Moreover, the ANN was able to evaluate the psychomotor skills of users into 2 classes: experienced or nonexperienced. We constructed and evaluated an affordable laparoscopic trainer system using computer vision, augmented reality, and an artificial intelligence algorithm. The proposed trainer has the potential to increase the self-confidence of trainees and to be applied to programs with limited resources.

Dual Rationality and Deliberative Agents

NASA Astrophysics Data System (ADS)

Debenham, John; Sierra, Carles

Human agents deliberate using models based on reason for only a minute proportion of the decisions that they make. In stark contrast, the deliberation of artificial agents is heavily dominated by formal models based on reason such as game theory, decision theory and logic—despite that fact that formal reasoning will not necessarily lead to superior real-world decisions. Further the Nobel Laureate Friedrich Hayek warns us of the ‘fatal conceit’ in controlling deliberative systems using models based on reason as the particular model chosen will then shape the system’s future and either impede, or eventually destroy, the subtle evolutionary processes that are an integral part of human systems and institutions, and are crucial to their evolution and long-term survival. We describe an architecture for artificial agents that is founded on Hayek’s two rationalities and supports the two forms of deliberation used by mankind.

From OLS to VIIRS, an overview of nighttime satellite aerosol retrievals using artificial light sources

NASA Astrophysics Data System (ADS)

Zhang, J.; Miller, S. D.; Reid, J. S.; Hyer, E. J.; McHardy, T. M.

2015-12-01

Compared to abundant daytime satellite-based observations of atmospheric aerosol, observations at night are relatively scarce. In particular, conventional satellite passive imaging radiometers, which offer expansive swaths of spatial coverage compared to non-scanning lidar systems, lack sensitivity to most aerosol types via the available thermal infrared bands available at night. In this talk, we make the fundamental case for the importance of nighttime aerosol information in forecast models, and the need to mitigate the existing nocturnal gap. We review early attempts at estimating nighttime aerosol optical properties using the modulation of stable artificial surface lights. Initial algorithm development using DMSP Operational Linescan System (OLS) has graduated to refined techniques based on the Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). We present examples of these retrievals for selected cases and compare the results to available surface-based point-source validation data.

Investigation of Age Polyethism in Food Processing of the Fungus-Growing Termite Odontotermes formosanus (Blattodea: Termitidae) Using a Laboratory Artificial Rearing System.

PubMed

Li, Hongjie; Yang, Mengyi; Chen, Yonger; Zhu, Na; Lee, Chow-Yang; Wei, Ji-Qian; Mo, Jianchu

2015-02-01

Laboratory rearing systems are useful models for studying Rhinotermitid behavior. Information on the biology of fungus-growing termites, however, is limited because of the difficulty of rearing colonies in the laboratory settings. The physical structure of termite nests makes it impossible to photograph or to observe colonies in the field. In this study, an artificial rearing system for field-collected colonies of the fungus-growing termite Odontotermes formosanus (Shiraki) was developed to facilitate observation in the laboratory. We recorded colony activity within the artificial rearing system and documented a variety of social behaviors that occurred throughout the food processing of the colony. This complex miniature ecosystem was cooperatively organized via division of labor in the foraging and processing of plant materials, and the observed patterns largely resembled the caste and age-based principles present in Macrotermes colonies. This work extends our insights into polyethism in the subfamily Macrotermitinae. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Materials for diabetes therapeutics.

PubMed

Bratlie, Kaitlin M; York, Roger L; Invernale, Michael A; Langer, Robert; Anderson, Daniel G

2012-05-01

This review is focused on the materials and methods used to fabricate closed-loop systems for type 1 diabetes therapy. Herein, we give a brief overview of current methods used for patient care and discuss two types of possible treatments and the materials used for these therapies-(i) artificial pancreases, comprised of insulin producing cells embedded in a polymeric biomaterial, and (ii) totally synthetic pancreases formulated by integrating continuous glucose monitors with controlled insulin release through degradable polymers and glucose-responsive polymer systems. Both the artificial and the completely synthetic pancreas have two major design requirements: the device must be both biocompatible and be permeable to small molecules and proteins, such as insulin. Several polymers and fabrication methods of artificial pancreases are discussed: microencapsulation, conformal coatings, and planar sheets. We also review the two components of a completely synthetic pancreas. Several types of glucose sensing systems (including materials used for electrochemical, optical, and chemical sensing platforms) are discussed, in addition to various polymer-based release systems (including ethylene-vinyl acetate, polyanhydrides, and phenylboronic acid containing hydrogels). Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

NASA Astrophysics Data System (ADS)

Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

2016-09-01

The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

Proceedings of the 1986 IEEE international conference on systems, man and cybernetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1986-01-01

This book presents the papers given at a conference on man-machine systems. Topics considered at the conference included neural model-based cognitive theory and engineering, user interfaces, adaptive and learning systems, human interaction with robotics, decision making, the testing and evaluation of expert systems, software development, international conflict resolution, intelligent interfaces, automation in man-machine system design aiding, knowledge acquisition in expert systems, advanced architectures for artificial intelligence, pattern recognition, knowledge bases, and machine vision.

A conceptual framework for intelligent real-time information processing

NASA Technical Reports Server (NTRS)

Schudy, Robert

1987-01-01

By combining artificial intelligence concepts with the human information processing model of Rasmussen, a conceptual framework was developed for real time artificial intelligence systems which provides a foundation for system organization, control and validation. The approach is based on the description of system processing terms of an abstraction hierarchy of states of knowledge. The states of knowledge are organized along one dimension which corresponds to the extent to which the concepts are expressed in terms of the system inouts or in terms of the system response. Thus organized, the useful states form a generally triangular shape with the sensors and effectors forming the lower two vertices and the full evaluated set of courses of action the apex. Within the triangle boundaries are numerous processing paths which shortcut the detailed processing, by connecting incomplete levels of analysis to partially defined responses. Shortcuts at different levels of abstraction include reflexes, sensory motor control, rule based behavior, and satisficing. This approach was used in the design of a real time tactical decision aiding system, and in defining an intelligent aiding system for transport pilots.

Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

NASA Astrophysics Data System (ADS)

Panda, Satyasen

2018-05-01

This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

Mechanical Transformation of Task Heuristics into Operational Procedures

DTIC Science & Technology

1981-04-14

Introduction A central theme of recent research in artificial intelligence is that *Intelligent task performance requires large amounts of knowledge...PLAY P1 C4] (. (LEADING (QSO)) (OR (CAN-LEAO- HEARrS (gSO)J (mEg (SUIT-OF C3) H])] C-) (FOLLOWING (QSO)) (OR [VOID (OSO) (SUIT-LED)3 [IN-SUIT C3 (SUIT...Production rules as a representation for a knowledge based consultation system. Artificial Intelligence 8:15-45, Spring, 1977. [Davis 77b] R. Davis

The Artificial Hamiltonian, First Integrals, and Closed-Form Solutions of Dynamical Systems for Epidemics

NASA Astrophysics Data System (ADS)

Naz, Rehana; Naeem, Imran

2018-03-01

The non-standard Hamiltonian system, also referred to as a partial Hamiltonian system in the literature, of the form {\\dot q^i} = {partial H}/{partial {p_i}},\\dot p^i = - {partial H}/{partial {q_i}} + {Γ ^i}(t,{q^i},{p_i}) appears widely in economics, physics, mechanics, and other fields. The non-standard (partial) Hamiltonian systems arise from physical Hamiltonian structures as well as from artificial Hamiltonian structures. We introduce the term `artificial Hamiltonian' for the Hamiltonian of a model having no physical structure. We provide here explicitly the notion of an artificial Hamiltonian for dynamical systems of ordinary differential equations (ODEs). Also, we show that every system of second-order ODEs can be expressed as a non-standard (partial) Hamiltonian system of first-order ODEs by introducing an artificial Hamiltonian. This notion of an artificial Hamiltonian gives a new way to solve dynamical systems of first-order ODEs and systems of second-order ODEs that can be expressed as a non-standard (partial) Hamiltonian system by using the known techniques applicable to the non-standard Hamiltonian systems. We employ the proposed notion to solve dynamical systems of first-order ODEs arising in epidemics.

A prototype system for perinatal knowledge engineering using an artificial intelligence tool.

PubMed

Sokol, R J; Chik, L

1988-01-01

Though several perinatal expert systems are extant, the use of artificial intelligence has, as yet, had minimal impact in medical computing. In this evaluation of the potential of AI techniques in the development of a computer based "Perinatal Consultant," a "top down" approach to the development of a perinatal knowledge base was taken, using as a source for such a knowledge base a 30-page manuscript of a chapter concerning high risk pregnancy. The UNIX utility "style" was used to parse sentences and obtain key words and phrases, both as part of a natural language interface and to identify key perinatal concepts. Compared with the "gold standard" of sentences containing key facts as chosen by the experts, a semiautomated method using a nonmedical speller to identify key words and phrases in context functioned with a sensitivity of 79%, i.e., approximately 8 in 10 key sentences were detected as the basis for PROLOG, rules and facts for the knowledge base. These encouraging results suggest that functional perinatal expert systems may well be expedited by using programming utilities in conjunction with AI tools and published literature.

The artificial membrane insert system as predictive tool for formulation performance evaluation.

PubMed

Berben, Philippe; Brouwers, Joachim; Augustijns, Patrick

2018-02-15

In view of the increasing interest of pharmaceutical companies for cell- and tissue-free models to implement permeation into formulation testing, this study explored the capability of an artificial membrane insert system (AMI-system) as predictive tool to evaluate the performance of absorption-enabling formulations. Firstly, to explore the usefulness of the AMI-system in supersaturation assessment, permeation was monitored after induction of different degrees of loviride supersaturation. Secondly, to explore the usefulness of the AMI-system in formulation evaluation, a two-stage dissolution test was performed prior to permeation assessment. Different case examples were selected based on the availability of in vivo (intraluminal and systemic) data: (i) a suspension of posaconazole (Noxafil ® ), (ii) a cyclodextrin-based formulation of itraconazole (Sporanox ® ), and (iii) a micronized (Lipanthyl ® ) and nanosized (Lipanthylnano ® ) formulation of fenofibrate. The obtained results demonstrate that the AMI-system is able to capture the impact of loviride supersaturation on permeation. Furthermore, the AMI-system correctly predicted the effects of (i) formulation pH on posaconazole absorption, (ii) dilution on cyclodextrin-based itraconazole absorption, and (iii) food intake on fenofibrate absorption. Based on the applied in vivo/in vitro approach, the AMI-system combined with simple dissolution testing appears to be a time- and cost-effective tool for the early-stage evaluation of absorption-enabling formulations. Copyright © 2017 Elsevier B.V. All rights reserved.

Fault Detection and Safety in Closed-Loop Artificial Pancreas Systems

PubMed Central

2014-01-01

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and continuous glucose monitor sensor signals can suffer from a variety of anomalies, including signal dropout and pressure-induced sensor attenuations. In addition to hardware-based failures, software and human-induced errors can cause safety-related problems. Techniques for fault detection, safety analyses, and remote monitoring techniques that have been applied in other industries and applications, such as chemical process plants and commercial aircraft, are discussed and placed in the context of a closed-loop artificial pancreas. PMID:25049365

SHRIF, a General-Purpose System for Heuristic Retrieval of Information and Facts, Applied to Medical Knowledge Processing.

ERIC Educational Resources Information Center

Findler, Nicholas V.; And Others

1992-01-01

Describes SHRIF, a System for Heuristic Retrieval of Information and Facts, and the medical knowledge base that was used in its development. Highlights include design decisions; the user-machine interface, including the language processor; and the organization of the knowledge base in an artificial intelligence (AI) project like this one. (57…

Impact of seasonality on artificial drainage discharge under temperate climate conditions

Treesearch

Ulrike Hirt; Annett Wetzig; Devandra Amatya; Marisa Matranga

2011-01-01

Artificial drainage systems affect all components of the water and matter balance. For the proper simulation of water and solute fluxes, information is needed about artificial drainage discharge rates and their response times. However, there is relatively little information available about the response of artificial drainage systems to precipitation. To address this...

The Joint Tactical Aerial Resupply Vehicle Impact on Sustainment Operations

DTIC Science & Technology

2017-06-09

Artificial Intelligence , Sustainment Operations, Rifle Company, Autonomous Aerial Resupply, Joint Tactical Autonomous Aerial Resupply System 16...Integrations and Development System AI Artificial Intelligence ARCIC Army Capabilities Integration Center ARDEC Armament Research, Development and...semi- autonomous systems, and fully autonomous systems. Autonomy of machines depends on sophisticated software, including Artificial Intelligence

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

14 CFR 23.691 - Artificial stall barrier system.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AIRCRAFT AIRWORTHINESS STANDARDS: NORMAL, UTILITY, ACROBATIC, AND COMMUTER CATEGORY AIRPLANES Design and Construction Control Systems § 23.691 Artificial stall barrier system. If the function of an artificial stall... downward pitching control will be provided must be established. (b) Considering the plus and minus airspeed...

Communications and control for electric power systems: Power system stability applications of artificial neural networks

NASA Technical Reports Server (NTRS)

Toomarian, N.; Kirkham, Harold

1994-01-01

This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

PubMed

Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

2015-01-01

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

Multi-Agent Framework for Virtual Learning Spaces.

ERIC Educational Resources Information Center

Sheremetov, Leonid; Nunez, Gustavo

1999-01-01

Discussion of computer-supported collaborative learning, distributed artificial intelligence, and intelligent tutoring systems focuses on the concept of agents, and describes a virtual learning environment that has a multi-agent system. Describes a model of interactions in collaborative learning and discusses agents for Web-based virtual…

System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Danshi; Zhang, Min; Li, Ze; Song, Chuang; Fu, Meixia; Li, Jin; Chen, Xue

2017-09-01

A bio-inspired detector based on the artificial neural network (ANN) and genetic algorithm is proposed in the context of a coherent optical transmission system. The ANN is designed to mitigate 16-quadrature amplitude modulation system impairments, including linear impairment: Gaussian white noise, laser phase noise, in-phase/quadrature component imbalance, and nonlinear impairment: nonlinear phase. Without prior information or heuristic assumptions, the ANN, functioning as a machine learning algorithm, can learn and capture the characteristics of impairments from observed data. Numerical simulations were performed, and dispersion-shifted, dispersion-managed, and dispersion-unmanaged fiber links were investigated. The launch power dynamic range and maximum transmission distance for the bio-inspired method were 2.7 dBm and 240 km greater, respectively, than those of the maximum likelihood estimation algorithm. Moreover, the linewidth tolerance of the bio-inspired technique was 170 kHz greater than that of the k-means method, demonstrating its usability for digital signal processing in coherent systems.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

PubMed

Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

2014-05-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

PubMed Central

Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

2013-01-01

One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt. PMID:25685507

Application of Artificial Neuro-Fuzzy Logic Inference System for Predicting the Microbiological Pollution in Fresh Water

NASA Astrophysics Data System (ADS)

Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.

The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.

An Investigation on the Role of Spike Latency in an Artificial Olfactory System

PubMed Central

Martinelli, Eugenio; Polese, Davide; Dini, Francesca; Paolesse, Roberto; Filippini, Daniel; Lundström, Ingemar; Di Natale, Corrado

2011-01-01

Experimental studies have shown that the reactions to external stimuli may appear only few hundreds of milliseconds after the physical interaction of the stimulus with the proper receptor. This behavior suggests that neurons transmit the largest meaningful part of their signal in the first spikes, and than that the spike latency is a good descriptor of the information content in biological neural networks. In this paper this property has been investigated in an artificial sensorial system where a single layer of spiking neurons is trained with the data generated by an artificial olfactory platform based on a large array of chemical sensors. The capability to discriminate between distinct chemicals and mixtures of them was studied with spiking neural networks endowed with and without lateral inhibitions and considering as output feature of the network both the spikes latency and the average firing rate. Results show that the average firing rate of the output spikes sequences shows the best separation among the experienced vapors, however the latency code is able in a shorter time to correctly discriminate all the tested volatile compounds. This behavior is qualitatively similar to those recently found in natural olfaction, and noteworthy it provides practical suggestions to tail the measurement conditions of artificial olfactory systems defining for each specific case a proper measurement time. PMID:22194721

Expert systems and simulation models; Proceedings of the Seminar, Tucson, AZ, November 18, 19, 1985

NASA Technical Reports Server (NTRS)

1986-01-01

The seminar presents papers on modeling and simulation methodology, artificial intelligence and expert systems, environments for simulation/expert system development, and methodology for simulation/expert system development. Particular attention is given to simulation modeling concepts and their representation, modular hierarchical model specification, knowledge representation, and rule-based diagnostic expert system development. Other topics include the combination of symbolic and discrete event simulation, real time inferencing, and the management of large knowledge-based simulation projects.

Progress and challenges in the application of artificial intelligence to computational fluid dynamics

NASA Technical Reports Server (NTRS)

Andrews, Alison E.

1987-01-01

An approach to analyzing CFD knowledge-based systems is proposed which is based, in part, on the concept of knowledge-level analysis. Consideration is given to the expert cooling fan design system, the PAN AIR knowledge system, grid adaptation, and expert zonal grid generation. These AI/CFD systems demonstrate that current AI technology can be successfully applied to well-formulated problems that are solved by means of classification or selection of preenumerated solutions.

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

PubMed

Zhang, Deming; Zeng, Lang; Cao, Kaihua; Wang, Mengxing; Peng, Shouzhong; Zhang, Yue; Zhang, Youguang; Klein, Jacques-Olivier; Wang, Yu; Zhao, Weisheng

2016-08-01

Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work.

Vacuum generation in pneumatic artificial heart drives with a specially designed ejector system.

PubMed

Schima, H; Huber, L; Spitaler, F

1990-06-01

To improve the filling characteristics of pneumatically driven membrane artificial hearts (AHs), a vacuum is applied during diastole. This paper describes an ejector system for AH-drivers based on the Venturi effect, which was designed for this purpose. It provides vacuums of more than -40 mmHg at flow rates up to 50 l/min requiring a supplying primary gas pressure of less than 150 kPa (1140 mmHg). Under normal working conditions, the necessary supply flow was less than 5l/min. The device is small, cheap, quiet and fail-safe, and has been evaluated successfully in experimental and clinical use.

Coordination or Collision? The Intersection of Diabetes Care, Cybersecurity, and Cloud-Based Computing.

PubMed

Thiel, Scott; Mitchell, Jennifer; Williams, Jim

2017-03-01

Diagnosis and treatment of diabetes changed little from the Middle Ages through the early 19th century, when the first chemical test for the condition was developed. In the 20th century, advances in diabetes management gained momentum with home-use diagnostic devices and mass-produced insulin. In the 21st century, technological developments around diabetes are advancing so rapidly that a small, discrete system of medical devices that serve as an artificial pancreas are now possible. In this article, we assert that medical device interoperability and cyber security are necessary preconditions for safe, effective, and reliable widespread use of the artificial pancreas system.

Some Aspects of Artificial Bodies Stabilization and Orientation

NASA Astrophysics Data System (ADS)

Samardzija, B.; Segan, S.

2012-12-01

To increase energy resources, and thus the overall possibility of modern cosmic aircrafts, power supply was expanded by adding the (moving) wing area and antenna with complex orientation and design. It is clear that all of this, when there is a need to conduct a very accurate account of orbital elements of satellites, is a nightmare for the experts and scientists. In this paper we will give special attention to the system of stabilization and orientation of satellites, as well as to the importance of gyroscopic effects and the navigation systems of the artificial celestial bodies. Development of modified practical solutions based on knowledge and experience with gyroscopic effects is immeasurable.

Method of mobile robot indoor navigation by artificial landmarks with use of computer vision

NASA Astrophysics Data System (ADS)

Glibin, E. S.; Shevtsov, A. A.; Enik, O. A.

2018-05-01

The article describes an algorithm of the mobile robot indoor navigation based on the use of visual odometry. The results of the experiment identifying calculation errors in the distance traveled on a slip are presented. It is shown that the use of computer vision allows one to correct erroneous coordinates of the robot with the help of artificial landmarks. The control system utilizing the proposed method has been realized on the basis of Arduino Mego 2560 controller and a single-board computer Raspberry Pi 3. The results of the experiment on the mobile robot navigation with the use of this control system are presented.

Coordination or Collision? The Intersection of Diabetes Care, Cybersecurity, and Cloud-Based Computing

PubMed Central

Thiel, Scott; Mitchell, Jennifer; Williams, Jim

2016-01-01

Diagnosis and treatment of diabetes changed little from the Middle Ages through the early 19th century, when the first chemical test for the condition was developed. In the 20th century, advances in diabetes management gained momentum with home-use diagnostic devices and mass-produced insulin. In the 21st century, technological developments around diabetes are advancing so rapidly that a small, discrete system of medical devices that serve as an artificial pancreas are now possible. In this article, we assert that medical device interoperability and cyber security are necessary preconditions for safe, effective, and reliable widespread use of the artificial pancreas system. PMID:27784829

Proceedings of the international conference on cybernetics and societ

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1985-01-01

This book presents the papers given at a conference on artificial intelligence, expert systems and knowledge bases. Topics considered at the conference included automating expert system development, modeling expert systems, causal maps, data covariances, robot vision, image processing, multiprocessors, parallel processing, VLSI structures, man-machine systems, human factors engineering, cognitive decision analysis, natural language, computerized control systems, and cybernetics.

Comparison of dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional insulin pump therapy for glycaemic control in patients with type 1 diabetes: an open-label randomised controlled crossover trial.

PubMed

Haidar, Ahmad; Legault, Laurent; Messier, Virginie; Mitre, Tina Maria; Leroux, Catherine; Rabasa-Lhoret, Rémi

2015-01-01

The artificial pancreas is an emerging technology for the treatment of type 1 diabetes and two configurations have been proposed: single-hormone (insulin alone) and dual-hormone (insulin and glucagon). We aimed to delineate the usefulness of glucagon in the artificial pancreas system. We did a randomised crossover trial of dual-hormone artificial pancreas, single-hormone artificial pancreas, and conventional insulin pump therapy (continuous subcutaneous insulin infusion) in participants aged 12 years or older with type 1 diabetes. Participants were assigned in a 1:1:1:1:1:1 ratio with blocked randomisation to the three interventions and attended a research facility for three 24-h study visits. During visits when the patient used the single-hormone artificial pancreas, insulin was delivered based on glucose sensor readings and a predictive dosing algorithm. During dual-hormone artificial pancreas visits, glucagon was also delivered during low or falling glucose. During conventional insulin pump therapy visits, patients received continuous subcutaneous insulin infusion. The study was not masked. The primary outcome was the time for which plasma glucose concentrations were in the target range (4·0-10·0 mmol/L for 2 h postprandially and 4·0-8·0 mmol/L otherwise). Hypoglycaemic events were defined as plasma glucose concentration of less than 3·3 mmol/L with symptoms or less than 3·0 mmol/L irrespective of symptoms. Analysis was by modified intention to treat, in which we included data for all patients who completed at least two visits. A p value of less than 0·0167 (0·05/3) was regarded as significant. This trial is registered with ClinicalTrials.gov, number NCT01754337. The mean proportion of time spent in the plasma glucose target range over 24 h was 62% (SD 18), 63% (18), and 51% (19) with single-hormone artificial pancreas, dual-hormone artificial pancreas, and conventional insulin pump therapy, respectively. The mean difference in time spent in the target range between single-hormone artificial pancreas and conventional insulin pump therapy was 11% (17; p=0·002) and between dual-hormone artificial pancreas and conventional insulin pump therapy was 12% (21; p=0·00011). There was no difference (15; p=0·75) in the proportion of time spent in the target range between the single-hormone and dual-hormone artificial pancreas systems. There were 52 hypoglycaemic events with conventional insulin pump therapy (12 of which were symptomatic), 13 with the single-hormone artificial pancreas (five of which were symptomatic), and nine with the dual-hormone artificial pancreas (0 of which were symptomatic); the number of nocturnal hypoglycaemic events was 13 (0 symptomatic), 0, and 0, respectively. Single-hormone and dual-hormone artificial pancreas systems both provided better glycaemic control than did conventional insulin pump therapy. The single-hormone artificial pancreas might be sufficient for hypoglycaemia-free overnight glycaemic control. Canadian Diabetes Association; Fondation J A De Sève; Juvenile Diabetes Research Foundation; and Medtronic. Copyright © 2015 Elsevier Ltd. All rights reserved.

Control design based on dead-zone and leakage adaptive laws for artificial swarm mechanical systems

NASA Astrophysics Data System (ADS)

Zhao, Xiaomin; Chen, Y. H.; Zhao, Han

2017-05-01

We consider the control design of artificial swarm systems with emphasis on four characteristics. First, the agent is made of mechanical components. As a result, the motion of each agent is subject to physical laws that govern mechanical systems. Second, both nonlinearity and uncertainty of the mechanical system are taken into consideration. Third, the ideal agent kinematic performance is treated as a desired d'Alembert constraint. This in turn suggests a creative way of embedding the constraint into the control design. Fourth, two types of adaptive robust control schemes are designed. They both contain leakage and dead-zone. However, one design suggests a trade-off between the amount of leakage and the size of dead-zone, in exchange for a simplified dead-zone structure.

Development of a microcontroller-based automatic control system for the electrohydraulic total artificial heart.

PubMed

Kim, H C; Khanwilkar, P S; Bearnson, G B; Olsen, D B

1997-01-01

An automatic physiological control system for the actively filled, alternately pumped ventricles of the volumetrically coupled, electrohydraulic total artificial heart (EHTAH) was developed for long-term use. The automatic control system must ensure that the device: 1) maintains a physiological response of cardiac output, 2) compensates for an nonphysiological condition, and 3) is stable, reliable, and operates at a high power efficiency. The developed automatic control system met these requirements both in vitro, in week-long continuous mock circulation tests, and in vivo, in acute open-chested animals (calves). Satisfactory results were also obtained in a series of chronic animal experiments, including 21 days of continuous operation of the fully automatic control mode, and 138 days of operation in a manual mode, in a 159-day calf implant.

Incomplete fuzzy data processing systems using artificial neural network

NASA Technical Reports Server (NTRS)

Patyra, Marek J.

1992-01-01

In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

An intelligent ground operator support system

NASA Technical Reports Server (NTRS)

Goerlach, Thomas; Ohlendorf, Gerhard; Plassmeier, Frank; Bruege, Uwe

1994-01-01

This paper presents first results of the project 'Technologien fuer die intelligente Kontrolle von Raumfahrzeugen' (TIKON). The TIKON objective was the demonstration of feasibility and profit of the application of artificial intelligence in the space business. For that purpose a prototype system has been developed and implemented for the operation support of the Roentgen Satellite (ROSAT), a scientific spacecraft designed to perform the first all-sky survey with a high-resolution X-ray telescope and to investigate the emission of specific celestial sources. The prototype integrates a scheduler and a diagnosis tool both based on artificial intelligence techniques. The user interface is menu driven and provides synoptic displays for the visualization of the system status. The prototype has been used and tested in parallel to an already existing operational system.

New intraocular pressure measurement method using reflected pneumatic pressure from cornea deformed by air puff of ring-type nozzle.

PubMed

Kim, Hyung Jin; Seo, Yeong Ho; Kim, Byeong Hee

2017-01-01

In this study, a non-contact type intraocular pressure (IOP) measuring system using reflected pneumatic pressure is proposed to overcome the disadvantages of existing measurement systems. A ring-type nozzle, a key component in the proposed system, is designed via computational fluid analysis. It predicts the reflected pneumatic pressure based on the nozzle exit angle and inner and outer diameters of the nozzle, which are 30°, 7 mm, and 9 mm, respectively. Performance evaluation is conducted using artificial eyes fabricated using polydimethylsiloxane with the specifications of human eyes. The IOP of the fabricated artificial eyes is adjusted to 10, 30, and 50 mm Hg, and the reflected pneumatic pressure is measured as a function of the distance between the ring-type nozzle and artificial eye. The measured reflected pneumatic pressure is high when the measurement distance is short and eye pressure is low. The cornea of an artificial eye is significantly deformed at a low IOP, and the applied pneumatic pressure is more concentrated in front of the ring-type nozzle because of the deformed cornea. Thus, the reflected pneumatic pressure at a low IOP has more inflows into the pressure sensor inserted inside the nozzle. The sensitivity of the output based on the IOP at measurement distances between 3-5 mm is -0.0027, -0.0022, -0.0018, -0.0015, and -0.0012. Sensitivity decreases as the measurement distance increases. In addition, the reflected pneumatic pressure owing to the misalignment at the measurement distances of 3-5 mm is not affected within a range of 0.5 mm. Therefore, the measurement range is acceptable up to a 1 mm diameter from the center of an artificial eye. However, the accuracy gradually decreases as the reflected pneumatic pressure from a misalignment of 1 mm or more decreases by 26% or more.

New intraocular pressure measurement method using reflected pneumatic pressure from cornea deformed by air puff of ring-type nozzle

PubMed Central

Kim, Hyung Jin; Seo, Yeong Ho

2017-01-01

In this study, a non-contact type intraocular pressure (IOP) measuring system using reflected pneumatic pressure is proposed to overcome the disadvantages of existing measurement systems. A ring-type nozzle, a key component in the proposed system, is designed via computational fluid analysis. It predicts the reflected pneumatic pressure based on the nozzle exit angle and inner and outer diameters of the nozzle, which are 30°, 7 mm, and 9 mm, respectively. Performance evaluation is conducted using artificial eyes fabricated using polydimethylsiloxane with the specifications of human eyes. The IOP of the fabricated artificial eyes is adjusted to 10, 30, and 50 mm Hg, and the reflected pneumatic pressure is measured as a function of the distance between the ring-type nozzle and artificial eye. The measured reflected pneumatic pressure is high when the measurement distance is short and eye pressure is low. The cornea of an artificial eye is significantly deformed at a low IOP, and the applied pneumatic pressure is more concentrated in front of the ring-type nozzle because of the deformed cornea. Thus, the reflected pneumatic pressure at a low IOP has more inflows into the pressure sensor inserted inside the nozzle. The sensitivity of the output based on the IOP at measurement distances between 3–5 mm is -0.0027, -0.0022, -0.0018, -0.0015, and -0.0012. Sensitivity decreases as the measurement distance increases. In addition, the reflected pneumatic pressure owing to the misalignment at the measurement distances of 3–5 mm is not affected within a range of 0.5 mm. Therefore, the measurement range is acceptable up to a 1 mm diameter from the center of an artificial eye. However, the accuracy gradually decreases as the reflected pneumatic pressure from a misalignment of 1 mm or more decreases by 26% or more. PMID:29216189

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

DOT National Transportation Integrated Search

1996-01-01

An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Susmikanti, Mike, E-mail: mike@batan.go.id; Sulistyo, Jos, E-mail: soj@batan.go.id

2014-09-30

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to developmore » code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.« less

Development of a Heuristic Knowledge Base for the Selection of Applicable or Relevant and Appropriate Environmental Requirements

DTIC Science & Technology

1992-09-01

David King. Expert Systems: Artificial Intelligence in Bins. New York: John Wiley and Sons Inc., 1985. 10. Hayes-Roth, Frederick, Donald A. Waterman...Technology (AU), Wright-Patterson AFB, OH, July 1992. 26. Simmons, Asa B. and Steven G. Chappel. " Artificial Intelligence - Defini- tion and Practice," IEEE...information on treatment standards is through the publication of the CERCLA Compane With Other Laws Manual and the Co endium of CERCIA ARARs Fact Sheets

Modified Method of Adaptive Artificial Viscosity for Solution of Gas Dynamics Problems on Parallel Computer Systems

NASA Astrophysics Data System (ADS)

Popov, Igor; Sukov, Sergey

2018-02-01

A modification of the adaptive artificial viscosity (AAV) method is considered. This modification is based on one stage time approximation and is adopted to calculation of gasdynamics problems on unstructured grids with an arbitrary type of grid elements. The proposed numerical method has simplified logic, better performance and parallel efficiency compared to the implementation of the original AAV method. Computer experiments evidence the robustness and convergence of the method to difference solution.

On DSS Implementation in the Dynamic Model of the Digital Oil field

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Khisamutdinov, Maksim V.; Kalyaev, Anatoly I.

2018-02-01

Decision support systems (DSS), especially based on the artificial intelligence (AI) techniques are been widely applied in different domains nowadays. In the paper we depict an approach of implementing DSS in to Digital Oil Field (DOF) dynamic model structure in order to reduce the human factor influence, considering the automation of all production processes to be the DOF model clue element. As the basic tool of data handling we propose the hybrid application on artificial neural networks and evolutional algorithms.

Artificial intelligence techniques for scheduling Space Shuttle missions

NASA Technical Reports Server (NTRS)

Henke, Andrea L.; Stottler, Richard H.

1994-01-01

Planning and scheduling of NASA Space Shuttle missions is a complex, labor-intensive process requiring the expertise of experienced mission planners. We have developed a planning and scheduling system using combinations of artificial intelligence knowledge representations and planning techniques to capture mission planning knowledge and automate the multi-mission planning process. Our integrated object oriented and rule-based approach reduces planning time by orders of magnitude and provides planners with the flexibility to easily modify planning knowledge and constraints without requiring programming expertise.

Advanced Artificial Intelligence Technology Testbed

NASA Technical Reports Server (NTRS)

Anken, Craig S.

1993-01-01

The Advanced Artificial Intelligence Technology Testbed (AAITT) is a laboratory testbed for the design, analysis, integration, evaluation, and exercising of large-scale, complex, software systems, composed of both knowledge-based and conventional components. The AAITT assists its users in the following ways: configuring various problem-solving application suites; observing and measuring the behavior of these applications and the interactions between their constituent modules; gathering and analyzing statistics about the occurrence of key events; and flexibly and quickly altering the interaction of modules within the applications for further study.

Coherent manipulation of a solid-state artificial atom with few photons.

PubMed

Giesz, V; Somaschi, N; Hornecker, G; Grange, T; Reznychenko, B; De Santis, L; Demory, J; Gomez, C; Sagnes, I; Lemaître, A; Krebs, O; Lanzillotti-Kimura, N D; Lanco, L; Auffeves, A; Senellart, P

2016-06-17

In a quantum network based on atoms and photons, a single atom should control the photon state and, reciprocally, a single photon should allow the coherent manipulation of the atom. Both operations require controlling the atom environment and developing efficient atom-photon interfaces, for instance by coupling the natural or artificial atom to cavities. So far, much attention has been drown on manipulating the light field with atomic transitions, recently at the few-photon limit. Here we report on the reciprocal operation and demonstrate the coherent manipulation of an artificial atom by few photons. We study a quantum dot-cavity system with a record cooperativity of 13. Incident photons interact with the atom with probability 0.95, which radiates back in the cavity mode with probability 0.96. Inversion of the atomic transition is achieved for 3.8 photons on average, showing that our artificial atom performs as if fully isolated from the solid-state environment.

Artificial neural network-aided image analysis system for cell counting.

PubMed

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

Plugin-docking system for autonomous charging using particle filter

NASA Astrophysics Data System (ADS)

Koyasu, Hiroshi; Wada, Masayoshi

2017-03-01

Autonomous charging of the robot battery is one of the key functions for the sake of expanding working areas of the robots. To realize it, most of existing systems use custom docking stations or artificial markers. By the other words, they can only charge on a few specific outlets. If the limit can be removed, working areas of the robots significantly expands. In this paper, we describe a plugin-docking system for the autonomous charging, which does not require any custom docking stations or artificial markers. A single camera is used for recognizing the 3D position of an outlet socket. A particle filter-based image tracking algorithm which is robust to the illumination change is applied. The algorithm is implemented on a robot with an omnidirectional moving system. The experimental results show the effectiveness of our system.

Fano effect in the transport of an artificial molecule

NASA Astrophysics Data System (ADS)

Norimoto, Shota; Nakamura, Shuji; Okazaki, Yuma; Arakawa, Tomonori; Asano, Kenichi; Onomitsu, Koji; Kobayashi, Kensuke; Kaneko, Nobu-hisa

2018-05-01

The Fano effect is a ubiquitous phenomenon arising from interference between a discrete energy state and an energy continuum. We explore this effect in an artificial molecule, namely, two lateral quantum dots (QDs) fabricated from a two-dimensional electron gas system and coupled in series. When the coupling between the leads and QDs is small, the charge stability diagram of the system shows a honeycomb lattice structure that is characteristic of a double QD system. As the coupling increases, a honeycomb structure consisting of the Fano resonances emerges. A numerical simulation based on the T-matrix method can satisfactorily reproduce our experimental observation. This report constitutes a clear example of the ubiquitous nature of the Fano effect in mesoscopic transport.

Ontological engineering versus metaphysics

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Tomanek, Roman; Mulawka, Jan

2011-10-01

It has been recognized that ontologies are a semantic version of world wide web and can be found in knowledge-based systems. A recent time survey of this field also suggest that practical artificial intelligence systems may be motivated by this research. Especially strong artificial intelligence as well as concept of homo computer can also benefit from their use. The main objective of this contribution is to present and review already created ontologies and identify the main advantages which derive such approach for knowledge management systems. We would like to present what ontological engineering borrows from metaphysics and what a feedback it can provide to natural language processing, simulations and modelling. The potential topics of further development from philosophical point of view is also underlined.

Pilot Inventory Complex Adaptive System (PICAS): An Artificial Life Approach to Managing Pilot Retention.

DTIC Science & Technology

1999-03-01

mates) and base their behaviors on this interactive information. This alone defines the nature of a complex adaptive system and it is based on this...world policy initiatives. 2.3.4. User Interaction Building the model with extensive user interaction gives the entire system a more appealing feel...complex behavior that hopefully mimics trends observed in reality . User interaction also allows for easier justification of assumptions used within

Microcomputer-Based Intelligent Tutoring Systems: An Assessment.

ERIC Educational Resources Information Center

Schaffer, John William

Computer-assisted instruction, while familiar to most teachers, has failed to become an effective self-motivating instructional tool. Developments in artificial intelligence, however, have provided new and better tools for exploring human knowledge acquisition and utilization. Expert system technology represents one of the most promising of these…

Molecular diversity of drinking water bacterial communities using 16S rRNA gene sequence analyses

EPA Science Inventory

Our understanding of the microbial community structure of drinking water distribution system has relied on culture-based methods. However, recent studies have suggested that the majority of bacteria inhabiting distribution systems are unable to grow on artificial media. The goal ...

Artificial Neural Network Analysis System

DTIC Science & Technology

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

DTIC Science & Technology

1990-06-01

NAVAL POSTGRADUATE SCHOOL Monterey, California ’-DTIC 0 ELECT f NMARO 5 191 N S, U, THESIS B . AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR...NO. NO. NO ACCESSION NO 11. TITLE (Include Security Classification) AN ARTIFICIAL NEURAL NETWORK CONTROL SYSTEM FOR SPACECRAFT ATTITUDE STABILIZATION...obsolete a U.S. G v pi.. iim n P.. oiice! toog-eo.5s43 i Approved for public release; distribution is unlimited. AN ARTIFICIAL NEURAL NETWORK CONTROL

Head mounted DMD based projection system for natural and prosthetic visual stimulation in freely moving rats.

PubMed

Arens-Arad, Tamar; Farah, Nairouz; Ben-Yaish, Shai; Zlotnik, Alex; Zalevsky, Zeev; Mandel, Yossi

2016-10-12

Novel technologies are constantly under development for vision restoration in blind patients. Many of these emerging technologies are based on the projection of high intensity light patterns at specific wavelengths, raising the need for the development of specialized projection systems. Here we present and characterize a novel projection system that meets the requirements for artificial retinal stimulation in rats and enables the recording of cortical responses. The system is based on a customized miniature Digital Mirror Device (DMD) for pattern projection, in both visible (525 nm) and NIR (915 nm) wavelengths, and a lens periscope for relaying the pattern directly onto the animal's retina. Thorough system characterization and the investigation of the effect of various parameters on obtained image quality were performed using ZEMAX. Simulation results revealed that images with an MTF higher than 0.8 were obtained with little effect of the vertex distance. Increased image quality was obtained at an optimal pupil diameter and smaller field of view. Visual cortex activity data was recorded simultaneously with pattern projection, further highlighting the importance of the system for prosthetic vision studies. This novel head mounted projection system may prove to be a vital tool in studying natural and artificial vision in behaving animals.

Head mounted DMD based projection system for natural and prosthetic visual stimulation in freely moving rats

PubMed Central

Arens-Arad, Tamar; Farah, Nairouz; Ben-Yaish, Shai; Zlotnik, Alex; Zalevsky, Zeev; Mandel, Yossi

2016-01-01

Novel technologies are constantly under development for vision restoration in blind patients. Many of these emerging technologies are based on the projection of high intensity light patterns at specific wavelengths, raising the need for the development of specialized projection systems. Here we present and characterize a novel projection system that meets the requirements for artificial retinal stimulation in rats and enables the recording of cortical responses. The system is based on a customized miniature Digital Mirror Device (DMD) for pattern projection, in both visible (525 nm) and NIR (915 nm) wavelengths, and a lens periscope for relaying the pattern directly onto the animal’s retina. Thorough system characterization and the investigation of the effect of various parameters on obtained image quality were performed using ZEMAX. Simulation results revealed that images with an MTF higher than 0.8 were obtained with little effect of the vertex distance. Increased image quality was obtained at an optimal pupil diameter and smaller field of view. Visual cortex activity data was recorded simultaneously with pattern projection, further highlighting the importance of the system for prosthetic vision studies. This novel head mounted projection system may prove to be a vital tool in studying natural and artificial vision in behaving animals. PMID:27731346

Head mounted DMD based projection system for natural and prosthetic visual stimulation in freely moving rats

NASA Astrophysics Data System (ADS)

Arens-Arad, Tamar; Farah, Nairouz; Ben-Yaish, Shai; Zlotnik, Alex; Zalevsky, Zeev; Mandel, Yossi

2016-10-01

Novel technologies are constantly under development for vision restoration in blind patients. Many of these emerging technologies are based on the projection of high intensity light patterns at specific wavelengths, raising the need for the development of specialized projection systems. Here we present and characterize a novel projection system that meets the requirements for artificial retinal stimulation in rats and enables the recording of cortical responses. The system is based on a customized miniature Digital Mirror Device (DMD) for pattern projection, in both visible (525 nm) and NIR (915 nm) wavelengths, and a lens periscope for relaying the pattern directly onto the animal’s retina. Thorough system characterization and the investigation of the effect of various parameters on obtained image quality were performed using ZEMAX. Simulation results revealed that images with an MTF higher than 0.8 were obtained with little effect of the vertex distance. Increased image quality was obtained at an optimal pupil diameter and smaller field of view. Visual cortex activity data was recorded simultaneously with pattern projection, further highlighting the importance of the system for prosthetic vision studies. This novel head mounted projection system may prove to be a vital tool in studying natural and artificial vision in behaving animals.

A Primer for Problem Solving Using Artificial Intelligence.

ERIC Educational Resources Information Center

Schell, George P.

1988-01-01

Reviews the development of artificial intelligence systems and the mechanisms used, including knowledge representation, programing languages, and problem processing systems. Eleven books and 6 journals are listed as sources of information on artificial intelligence. (23 references) (CLB)

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Learning from Multiple Collaborating Intelligent Tutors: An Agent-based Approach.

ERIC Educational Resources Information Center

Solomos, Konstantinos; Avouris, Nikolaos

1999-01-01

Describes an open distributed multi-agent tutoring system (MATS) and discusses issues related to learning in such open environments. Topics include modeling a one student-many teachers approach in a computer-based learning context; distributed artificial intelligence; implementation issues; collaboration; and user interaction. (Author/LRW)

Expert Systems Based Clinical Assessment and Tutorial Project.

ERIC Educational Resources Information Center

Papa, Frank; Shores, Jay

This project at the Texas College of Osteopathic Medicine (Fort Worth) evaluated the use of an artificial-intelligence-derived measure, "Knowledge-Based Inference Tool" (KBIT), as the basis for assessing medical students' diagnostic capabilities and designing instruction to improve diagnostic skills. The instrument was designed to…

Rule-based mechanisms of learning for intelligent adaptive flight control

NASA Technical Reports Server (NTRS)

Handelman, David A.; Stengel, Robert F.

1990-01-01

How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

AiGERM: A logic programming front end for GERM

NASA Technical Reports Server (NTRS)

Hashim, Safaa H.

1990-01-01

AiGerm (Artificially Intelligent Graphical Entity Relation Modeler) is a relational data base query and programming language front end for MCC (Mission Control Center)/STP's (Space Test Program) Germ (Graphical Entity Relational Modeling) system. It is intended as an add-on component of the Germ system to be used for navigating very large networks of information. It can also function as an expert system shell for prototyping knowledge-based systems. AiGerm provides an interface between the programming language and Germ.

Artificial Intelligence Measurement System, Overview and Lessons Learned. Final Project Report.

ERIC Educational Resources Information Center

Baker, Eva L.; Butler, Frances A.

This report summarizes the work conducted for the Artificial Intelligence Measurement System (AIMS) Project which was undertaken as an exploration of methodology to consider how the effects of artificial intelligence systems could be compared to human performance. The research covered four areas of inquiry: (1) natural language processing and…

Multisensory Public Access Catalogs on CD-ROM.

ERIC Educational Resources Information Center

Harrison, Nancy; Murphy, Brower

1987-01-01

BiblioFile Intelligent Catalog is a CD-ROM-based public access catalog system which incorporates graphics and sound to provide a multisensory interface and artificial intelligence techniques to increase search precision. The system can be updated frequently and inexpensively by linking hard disk drives to CD-ROM optical drives. (MES)

Educational Game Systems in Artificial Intelligence Course

ERIC Educational Resources Information Center

Chubarkova, Elena V.; Sadchikov, Ilya A.; Suslova, Irina A.; Tsaregorodtsev, Andrey ?.; Milova, Larisa N.

2016-01-01

Article actuality based on fact that existing knowledge system aimed at future professional life of students: a skillful use game activity in educational process will teach students to look for alternative ways solving of real problems. The purpose of article lies in theoretical substantiation, development and testing of criteria, which must be…

Stability of Bifurcating Stationary Solutions of the Artificial Compressible System

NASA Astrophysics Data System (ADS)

Teramoto, Yuka

2018-02-01

The artificial compressible system gives a compressible approximation of the incompressible Navier-Stokes system. The latter system is obtained from the former one in the zero limit of the artificial Mach number ɛ which is a singular limit. The sets of stationary solutions of both systems coincide with each other. It is known that if a stationary solution of the incompressible system is asymptotically stable and the velocity field of the stationary solution satisfies an energy-type stability criterion, then it is also stable as a solution of the artificial compressible one for sufficiently small ɛ . In general, the range of ɛ shrinks when the spectrum of the linearized operator for the incompressible system approaches to the imaginary axis. This can happen when a stationary bifurcation occurs. It is proved that when a stationary bifurcation from a simple eigenvalue occurs, the range of ɛ can be taken uniformly near the bifurcation point to conclude the stability of the bifurcating solution as a solution of the artificial compressible system.

Establishment of an Artificial Tick Feeding System to Study Theileria lestoquardi Infection

PubMed Central

Tajeri, Shahin; Razmi, Gholamreza; Haghparast, Alireza

2016-01-01

The establishment of good experimental models for Theileria sp. infection is important for theileriosis research. Routinely, infection of ticks is accomplished by feeding on parasite-infected animals (sheep, cows and horses), which raises practical and ethical problems, driving the search for alternative methods of tick infection. Artificial tick feeding systems are based mainly on rearing ticks on host-derived or hand-made artificial membranes. We developed a modified feeding assay for infecting nymphal stages of Hyalomma anatolicum ticks with Theileria lestoquardi, a highly pathogenic parasite of sheep. We compared two different membranes: an artificial silicone membrane and a natural alternative using mouse skin. We observed high attachment rates with mouse skin, whereas in vitro feeding of H. anatolicum nymphs on silicone membranes was unsuccessful. We could infect H. anatolicum nymphs with T. lestoquardi and the emerging adult ticks transmitted infective parasites to sheep. In contrast, similar infections with Rhipicephalus bursa, a representative tick with short mouth-parts that was proposed as a vector for T. lestoquardi, appeared not to be a competent vector tick species. This is the first report of an experimentally controlled infection of H. anatolicum with T. lestoquardi and opens avenues to explore tick-parasite dynamics in detail. PMID:28036364

An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases.

PubMed

Sengur, Abdulkadir

2008-03-01

In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.

Are artificial neural networks black boxes?

PubMed

Benitez, J M; Castro, J L; Requena, I

1997-01-01

Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

Application of a novel particle tracking algorithm in the flow visualization of an artificial abdominal aortic aneurysm.

PubMed

Zhang, Yang; Wang, Yuan; He, Wenbo; Yang, Bin

2014-01-01

A novel Particle Tracking Velocimetry (PTV) algorithm based on Voronoi Diagram (VD) is proposed and briefed as VD-PTV. The robustness of VD-PTV for pulsatile flow is verified through a test that includes a widely used artificial flow and a classic reference algorithm. The proposed algorithm is then applied to visualize the flow in an artificial abdominal aortic aneurysm included in a pulsatile circulation system that simulates the aortic blood flow in human body. Results show that, large particles tend to gather at the upstream boundary because of the backflow eddies that follow the pulsation. This qualitative description, together with VD-PTV, has laid a foundation for future works that demand high-level quantification.

Swimming like algae: biomimetic soft artificial cilia

PubMed Central

Sareh, Sina; Rossiter, Jonathan; Conn, Andrew; Drescher, Knut; Goldstein, Raymond E.

2013-01-01

Cilia are used effectively in a wide variety of biological systems from fluid transport to thrust generation. Here, we present the design and implementation of artificial cilia, based on a biomimetic planar actuator using soft-smart materials. This actuator is modelled on the cilia movement of the alga Volvox, and represents the cilium as a piecewise constant-curvature robotic actuator that enables the subsequent direct translation of natural articulation into a multi-segment ionic polymer metal composite actuator. It is demonstrated how the combination of optimal segmentation pattern and biologically derived per-segment driving signals reproduce natural ciliary motion. The amenability of the artificial cilia to scaling is also demonstrated through the comparison of the Reynolds number achieved with that of natural cilia. PMID:23097503

Development and Evaluation of an Adaptive Computerized Training System (ACTS). R&D Report 78-1.

ERIC Educational Resources Information Center

Knerr, Bruce W.; Nawrocki, Leon H.

This report describes the development of a computer based system designed to train electronic troubleshooting procedures. The ACTS uses artificial intelligence techniques to develop models of student and expert troubleshooting behavior as they solve a series of troubleshooting problems on the system. Comparisons of the student and expert models…

Hybrid Architectures and Their Impact on Intelligent Design

NASA Technical Reports Server (NTRS)

Kandel, Abe

1996-01-01

In this presentation we investigate a novel framework for the design of autonomous fuzzy intelligent systems. The system integrates the following modules into a single autonomous entity: (1) a fuzzy expert system; (2) artificial neural network; (3) genetic algorithm; and (4) case-base reasoning. We describe the integration of these units into one intelligent structure and discuss potential applications.

Using a Recommender System and Hyperwave Attributes To Augment an Electronic Resource Library.

ERIC Educational Resources Information Center

Fenn, B.; Lennon, J.

There has been increasing interest over the past few years in systems that help users exchange recommendations about World Wide Web documents. Programs have ranged from those that rely totally on user pre-selection, to others that are based on artificial intelligence. This paper proposes a system that falls between these two extremes, providing…

Terraforming the Moon: a Viable Step in the Colonization of the Solar System?

NASA Astrophysics Data System (ADS)

Renn, H. W.

2002-01-01

One potential option for the colonization of other celestial bodies is Terraforming. The latter involves, as a first step, the creation of a breathable, artificial atmosphere. While terraforming other planets, especially Mars, has been under discussion for several decades, applying the same concept to Earth's closest neighbor, namely the Moon, plays virtually no role in existing plans for space colonization. This paper investigates the technical and economical feasibility of supplying the Moon with an artificial atmosphere. Based on existing concepts for life support systems, essential requirements for an artificial Lunar atmosphere are defined. Various alternatives for the atmospheric composition are investigated and the parameters of a preferred `reference atmosphere' are described in detail. In order to assess the latter's habitability, particularly with respect to wind speeds and temperature cycles, the Moon's wind system and temperature field are analyzed by using a customized climate simulation model. Aspects of technical feasibility are evaluated and major obstacles are identified. Finally, various assessment criteria with particular respect to economical and ethical considerations are discussed and preliminary conclusions are presented.

Modeling of lighting behaviour of a hybrid lighting system in inner spaces of Building of Electrical Engineering

NASA Astrophysics Data System (ADS)

Amado, L.; Osma, G.; Villamizar, R.

2016-07-01

This paper presents the modelling of lighting behaviour of a hybrid lighting system - HLS in inner spaces for tropical climate. HLS aims to mitigate the problem of high electricity consumption used by artificial lighting in buildings. These systems integrate intelligently the daylight and artificial light through control strategies. However, selection of these strategies usually depends on expertise of designer and of available budget. In order to improve the selection process of the control strategies, this paper analyses the Electrical Engineering Building (EEB) case, initially modelling of lighting behaviour is established for the HLS of a classroom and an office. This allows estimating the illuminance level of the mixed lighting in the space, and energy consumption by artificial light according to different lighting control techniques, a control strategy based on occupancy and a combination of them. The model considers the concept of Daylight Factor (DF) for the estimating of daylight illuminance on the work plane for tropical climatic conditions. The validation of the model was carried out by comparing the measured and model-estimated indoor illuminances.

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations

PubMed Central

Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge. PMID:27313604

An Artificial Intelligence System to Predict Quality of Service in Banking Organizations.

PubMed

Castelli, Mauro; Manzoni, Luca; Popovič, Aleš

2016-01-01

Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge.

Distributed intrusion detection system based on grid security model

NASA Astrophysics Data System (ADS)

Su, Jie; Liu, Yahui

2008-03-01

Grid computing has developed rapidly with the development of network technology and it can solve the problem of large-scale complex computing by sharing large-scale computing resource. In grid environment, we can realize a distributed and load balance intrusion detection system. This paper first discusses the security mechanism in grid computing and the function of PKI/CA in the grid security system, then gives the application of grid computing character in the distributed intrusion detection system (IDS) based on Artificial Immune System. Finally, it gives a distributed intrusion detection system based on grid security system that can reduce the processing delay and assure the detection rates.

Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications

NASA Astrophysics Data System (ADS)

Romeira, Bruno; Figueiredo, José M. L.; Javaloyes, Julien

2017-11-01

With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.

Delay dynamics of neuromorphic optoelectronic nanoscale resonators: Perspectives and applications.

PubMed

Romeira, Bruno; Figueiredo, José M L; Javaloyes, Julien

2017-11-01

With the recent exponential growth of applications using artificial intelligence (AI), the development of efficient and ultrafast brain-like (neuromorphic) systems is crucial for future information and communication technologies. While the implementation of AI systems using computer algorithms of neural networks is emerging rapidly, scientists are just taking the very first steps in the development of the hardware elements of an artificial brain, specifically neuromorphic microchips. In this review article, we present the current state of the art of neuromorphic photonic circuits based on solid-state optoelectronic oscillators formed by nanoscale double barrier quantum well resonant tunneling diodes. We address, both experimentally and theoretically, the key dynamic properties of recently developed artificial solid-state neuron microchips with delayed perturbations and describe their role in the study of neural activity and regenerative memory. This review covers our recent research work on excitable and delay dynamic characteristics of both single and autaptic (delayed) artificial neurons including all-or-none response, spike-based data encoding, storage, signal regeneration and signal healing. Furthermore, the neural responses of these neuromorphic microchips display all the signatures of extended spatio-temporal localized structures (LSs) of light, which are reviewed here in detail. By taking advantage of the dissipative nature of LSs, we demonstrate potential applications in optical data reconfiguration and clock and timing at high-speeds and with short transients. The results reviewed in this article are a key enabler for the development of high-performance optoelectronic devices in future high-speed brain-inspired optical memories and neuromorphic computing.

Knowledge Based Engineering for Spatial Database Management and Use

NASA Technical Reports Server (NTRS)

Peuquet, D. (Principal Investigator)

1984-01-01

The use of artificial intelligence techniques that are applicable to Geographic Information Systems (GIS) are examined. Questions involving the performance and modification to the database structure, the definition of spectra in quadtree structures and their use in search heuristics, extension of the knowledge base, and learning algorithm concepts are investigated.

Towards an Intelligent Planning Knowledge Base Development Environment

NASA Technical Reports Server (NTRS)

Chien, S.

1994-01-01

ract describes work in developing knowledge base editing and debugging tools for the Multimission VICAR Planner (MVP) system. MVP uses artificial intelligence planning techniques to automatically construct executable complex image processing procedures (using models of the smaller constituent image processing requests made to the JPL Multimission Image Processing Laboratory.

Computer Assisted Instructional Design for Computer-Based Instruction. Final Report. Working Papers.

ERIC Educational Resources Information Center

Russell, Daniel M.; Pirolli, Peter

Recent advances in artificial intelligence and the cognitive sciences have made it possible to develop successful intelligent computer-aided instructional systems for technical and scientific training. In addition, computer-aided design (CAD) environments that support the rapid development of such computer-based instruction have also been recently…

Dynamical Systems and Motion Vision.

DTIC Science & Technology

1988-04-01

TASK Artificial Inteligence Laboratory AREA I WORK UNIT NUMBERS 545 Technology Square . Cambridge, MA 02139 C\\ II. CONTROLLING OFFICE NAME ANO0 ADDRESS...INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY A.I.Memo No. 1037 April, 1988 Dynamical Systems and Motion Vision Joachim Heel Abstract: In this... Artificial Intelligence L3 Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory’s [1 Artificial Intelligence Research is

Planning representation for automated exploratory data analysis

NASA Astrophysics Data System (ADS)

St. Amant, Robert; Cohen, Paul R.

1994-03-01

Igor is a knowledge-based system for exploratory statistical analysis of complex systems and environments. Igor has two related goals: to help automate the search for interesting patterns in data sets, and to help develop models that capture significant relationships in the data. We outline a language for Igor, based on techniques of opportunistic planning, which balances control and opportunism. We describe the application of Igor to the analysis of the behavior of Phoenix, an artificial intelligence planning system.

Experience in using a numerical scheme with artificial viscosity at solving the Riemann problem for a multi-fluid model of multiphase flow

NASA Astrophysics Data System (ADS)

Bulovich, S. V.; Smirnov, E. M.

2018-05-01

The paper covers application of the artificial viscosity technique to numerical simulation of unsteady one-dimensional multiphase compressible flows on the base of the multi-fluid approach. The system of the governing equations is written under assumption of the pressure equilibrium between the "fluids" (phases). No interfacial exchange is taken into account. A model for evaluation of the artificial viscosity coefficient that (i) assumes identity of this coefficient for all interpenetrating phases and (ii) uses the multiphase-mixture Wood equation for evaluation of a scale speed of sound has been suggested. Performance of the artificial viscosity technique has been evaluated via numerical solution of a model problem of pressure discontinuity breakdown in a three-fluid medium. It has been shown that a relatively simple numerical scheme, explicit and first-order, combined with the suggested artificial viscosity model, predicts a physically correct behavior of the moving shock and expansion waves, and a subsequent refinement of the computational grid results in a monotonic approaching to an asymptotic time-dependent solution, without non-physical oscillations.

Artificial Muscles Based on Electroactive Polymers as an Enabling Tool in Biomimetics

NASA Technical Reports Server (NTRS)

Bar-Cohen, Y.

2007-01-01

Evolution has resolved many of nature's challenges leading to working and lasting solutions that employ principles of physics, chemistry, mechanical engineering, materials science, and many other fields of science and engineering. Nature's inventions have always inspired human achievements leading to effective materials, structures, tools, mechanisms, processes, algorithms, methods, systems, and many other benefits. Some of the technologies that have emerged include artificial intelligence, artificial vision, and artificial muscles, where the latter is the moniker for electroactive polymers (EAPs). To take advantage of these materials and make them practical actuators, efforts are made worldwide to develop capabilities that are critical to the field infrastructure. Researchers are developing analytical model and comprehensive understanding of EAP materials response mechanism as well as effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available; however, in recent years, significant progress has been made and commercial products have already started to appear. In the current paper, the state-of-the-art and challenges to artificial muscles as well as their potential application to biomimetic mechanisms and devices are described and discussed.

Microsoft kinect-based artificial perception system for control of functional electrical stimulation assisted grasping.

PubMed

Strbac, Matija; Kočović, Slobodan; Marković, Marko; Popović, Dejan B

2014-01-01

We present a computer vision algorithm that incorporates a heuristic model which mimics a biological control system for the estimation of control signals used in functional electrical stimulation (FES) assisted grasping. The developed processing software acquires the data from Microsoft Kinect camera and implements real-time hand tracking and object analysis. This information can be used to identify temporal synchrony and spatial synergies modalities for FES control. Therefore, the algorithm acts as artificial perception which mimics human visual perception by identifying the position and shape of the object with respect to the position of the hand in real time during the planning phase of the grasp. This artificial perception used within the heuristically developed model allows selection of the appropriate grasp and prehension. The experiments demonstrate that correct grasp modality was selected in more than 90% of tested scenarios/objects. The system is portable, and the components are low in cost and robust; hence, it can be used for the FES in clinical or even home environment. The main application of the system is envisioned for functional electrical therapy, that is, intensive exercise assisted with FES.

Microsoft Kinect-Based Artificial Perception System for Control of Functional Electrical Stimulation Assisted Grasping

PubMed Central

Kočović, Slobodan; Popović, Dejan B.

2014-01-01

We present a computer vision algorithm that incorporates a heuristic model which mimics a biological control system for the estimation of control signals used in functional electrical stimulation (FES) assisted grasping. The developed processing software acquires the data from Microsoft Kinect camera and implements real-time hand tracking and object analysis. This information can be used to identify temporal synchrony and spatial synergies modalities for FES control. Therefore, the algorithm acts as artificial perception which mimics human visual perception by identifying the position and shape of the object with respect to the position of the hand in real time during the planning phase of the grasp. This artificial perception used within the heuristically developed model allows selection of the appropriate grasp and prehension. The experiments demonstrate that correct grasp modality was selected in more than 90% of tested scenarios/objects. The system is portable, and the components are low in cost and robust; hence, it can be used for the FES in clinical or even home environment. The main application of the system is envisioned for functional electrical therapy, that is, intensive exercise assisted with FES. PMID:25202707

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored atmore » synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.« less

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-07-01

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called "Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres", (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the "Robust design of artificial neural networks methodology" and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system.

Knowledge-based diagnosis for aerospace systems

NASA Technical Reports Server (NTRS)

Atkinson, David J.

1988-01-01

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

Current topics in glycemic control by wearable artificial pancreas or bedside artificial pancreas with closed-loop system.

PubMed

Hanazaki, Kazuhiro; Munekage, Masaya; Kitagawa, Hiroyuki; Yatabe, Tomoaki; Munekage, Eri; Shiga, Mai; Maeda, Hiromichi; Namikawa, Tsutomu

2016-09-01

The incidence of diabetes is increasing at an unprecedented pace and has become a serious health concern worldwide during the last two decades. Despite this, adequate glycemic control using an artificial pancreas has not been established, although the 21st century has seen rapid developments in this area. Herein, we review current topics in glycemic control for both the wearable artificial pancreas for type 1 and type 2 diabetic patients and the bedside artificial pancreas for surgical diabetic patients. In type 1 diabetic patients, nocturnal hypoglycemia associated with insulin therapy remains a serious problem that could be addressed by the recent development of a wearable artificial pancreas. This smart phone-like device, comprising a real-time, continuous glucose monitoring system and insulin pump system, could potentially significantly reduce nocturnal hypoglycemia compared with conventional glycemic control. Of particular interest in this space are the recent inventions of a low-glucose suspend feature in the portable systems that automatically stops insulin delivery 2 h following a glucose sensor value <70 mg/dL and a bio-hormonal pump system consisting of insulin and glucagon pumps. Perioperative tight glycemic control using a bedside artificial pancreas with the closed-loop system has also proved safe and effective for not only avoiding hypoglycemia, but also for reducing blood glucose level variability resulting in good surgical outcomes. We hope that a more sophisticated artificial pancreas with closed-loop system will now be taken up for routine use worldwide, providing enormous relief for patients suffering from uncontrolled hyperglycemia, hypoglycemia, and/or variability in blood glucose concentrations.

Artificial vision support system (AVS(2)) for improved prosthetic vision.

PubMed

Fink, Wolfgang; Tarbell, Mark A

2014-11-01

State-of-the-art and upcoming camera-driven, implanted artificial vision systems provide only tens to hundreds of electrodes, affording only limited visual perception for blind subjects. Therefore, real time image processing is crucial to enhance and optimize this limited perception. Since tens or hundreds of pixels/electrodes allow only for a very crude approximation of the typically megapixel optical resolution of the external camera image feed, the preservation and enhancement of contrast differences and transitions, such as edges, are especially important compared to picture details such as object texture. An Artificial Vision Support System (AVS(2)) is devised that displays the captured video stream in a pixelation conforming to the dimension of the epi-retinal implant electrode array. AVS(2), using efficient image processing modules, modifies the captured video stream in real time, enhancing 'present but hidden' objects to overcome inadequacies or extremes in the camera imagery. As a result, visual prosthesis carriers may now be able to discern such objects in their 'field-of-view', thus enabling mobility in environments that would otherwise be too hazardous to navigate. The image processing modules can be engaged repeatedly in a user-defined order, which is a unique capability. AVS(2) is directly applicable to any artificial vision system that is based on an imaging modality (video, infrared, sound, ultrasound, microwave, radar, etc.) as the first step in the stimulation/processing cascade, such as: retinal implants (i.e. epi-retinal, sub-retinal, suprachoroidal), optic nerve implants, cortical implants, electric tongue stimulators, or tactile stimulators.

Application of artificial intelligence to pharmacy and medicine.

PubMed

Dasta, J F

1992-04-01

Artificial intelligence (AI) is a branch of computer science dealing with solving problems using symbolic programming. It has evolved into a problem solving science with applications in business, engineering, and health care. One application of AI is expert system development. An expert system consists of a knowledge base and inference engine, coupled with a user interface. A crucial aspect of expert system development is knowledge acquisition and implementing computable ways to solve problems. There have been several expert systems developed in medicine to assist physicians with medical diagnosis. Recently, several programs focusing on drug therapy have been described. They provide guidance on drug interactions, drug therapy monitoring, and drug formulary selection. There are many aspects of pharmacy that AI can have an impact on and the reader is challenged to consider these possibilities because they may some day become a reality in pharmacy.

Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

NASA Astrophysics Data System (ADS)

Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

2014-06-01

This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

Software Analyzes Complex Systems in Real Time

NASA Technical Reports Server (NTRS)

2008-01-01

Expert system software programs, also known as knowledge-based systems, are computer programs that emulate the knowledge and analytical skills of one or more human experts, related to a specific subject. SHINE (Spacecraft Health Inference Engine) is one such program, a software inference engine (expert system) designed by NASA for the purpose of monitoring, analyzing, and diagnosing both real-time and non-real-time systems. It was developed to meet many of the Agency s demanding and rigorous artificial intelligence goals for current and future needs. NASA developed the sophisticated and reusable software based on the experience and requirements of its Jet Propulsion Laboratory s (JPL) Artificial Intelligence Research Group in developing expert systems for space flight operations specifically, the diagnosis of spacecraft health. It was designed to be efficient enough to operate in demanding real time and in limited hardware environments, and to be utilized by non-expert systems applications written in conventional programming languages. The technology is currently used in several ongoing NASA applications, including the Mars Exploration Rovers and the Spacecraft Health Automatic Reasoning Pilot (SHARP) program for the diagnosis of telecommunication anomalies during the Neptune Voyager Encounter. It is also finding applications outside of the Space Agency.

[Is polytrauma affordable these days? G-DRG system vs per diem charge based on 1,030 patients with multiple injuries].

PubMed

Qvick, B; Buehren, V; Woltmann, A

2012-10-01

The introduction of diagnosis-related groups (DRG) in Germany comprises the risk of a non-cost-effective reimbursement in complex medical treatments. The aim of this study was to compare the reimbursement between the DRG system and the system of hospital per diem charge in effect until now. The G-DRG (Version 2004) reimbursement was calculated for 1,030 polytrauma patients (average ISS 26.4) treated at the BGU Murnau from 2000 to 2004, using a base value of 2900 euros, and compared to the reimbursement of hospital per diem charge. Just half of all polytrauma patients are classified as a polytrauma according to the DRG (18.7%) or as requiring artificial respiration based on the DRG (29.1%). The average G-DRG reimbursement was 27,157 euros vs 36,387 euros (74.6%). Patients with minor trauma, increasing age, high GCS, ICU stay without artificial respiration, trauma of the upper extremity and patients who survived show the greatest discrepancy. A revision of the G-DRG definition of polytrauma is necessary to ensure adequate reimbursement for management of patients with multiple injuries. The severity of a trauma has to be considered in the DRG system.

In Pursuit of Artificial Intelligence.

ERIC Educational Resources Information Center

Watstein, Sarah; Kesselman, Martin

1986-01-01

Defines artificial intelligence and reviews current research in natural language processing, expert systems, and robotics and sensory systems. Discussion covers current commercial applications of artificial intelligence and projections of uses and limitations in library technical and public services, e.g., in cataloging and online information and…

Optimal Solution for an Engineering Applications Using Modified Artificial Immune System

NASA Astrophysics Data System (ADS)

Padmanabhan, S.; Chandrasekaran, M.; Ganesan, S.; patan, Mahamed Naveed Khan; Navakanth, Polina

2017-03-01

An Engineering optimization leads a essential role in several engineering application areas like process design, product design, re-engineering and new product development, etc. In engineering, an awfully best answer is achieved by comparison to some completely different solutions by utilization previous downside information. An optimization algorithms provide systematic associate degreed economical ways that within which of constructing and comparison new design solutions so on understand at best vogue, thus on best solution efficiency and acquire the foremost wonderful design impact. In this paper, a new evolutionary based Modified Artificial Immune System (MAIS) algorithm used to optimize an engineering application of gear drive design. The results are compared with existing design.

Artificially Expanded Genetic Information Systems for New Aptamer Technologies.

PubMed

Biondi, Elisa; Benner, Steven A

2018-05-09

Directed evolution was first applied to diverse libraries of DNA and RNA molecules a quarter century ago in the hope of gaining technology that would allow the creation of receptors, ligands, and catalysts on demand. Despite isolated successes, the outputs of this technology have been somewhat disappointing, perhaps because the four building blocks of standard DNA and RNA have too little functionality to have versatile binding properties, and offer too little information density to fold unambiguously. This review covers the recent literature that seeks to create an improved platform to support laboratory Darwinism, one based on an artificially expanded genetic information system (AEGIS) that adds independently replicating nucleotide “letters” to the evolving “alphabet”.

The effect of the size of the system, aspect ratio and impurities concentration on the dynamic of emergent magnetic monopoles in artificial spin ice systems

NASA Astrophysics Data System (ADS)

León, Alejandro

2013-08-01

In this work we study the dynamical properties of a finite array of nanomagnets in artificial kagome spin ice at room temperature. The dynamic response of the array of nanomagnets is studied by implementing a "frustrated celular autómata" (FCA), based in the charge model and dipolar model. The FCA simulations allow us to study in real-time and deterministic way, the dynamic of the system, with minimal computational resource. The update function is defined according to the coordination number of vertices in the system. Our results show that for a set geometric parameters of the array of nanomagnets, the system exhibits high density of Dirac strings and high density emergent magnetic monopoles. A study of the effect of disorder in the arrangement of nanomagnets is incorporated in this work.

Artificial immune system via Euclidean Distance Minimization for anomaly detection in bearings

NASA Astrophysics Data System (ADS)

Montechiesi, L.; Cocconcelli, M.; Rubini, R.

2016-08-01

In recent years new diagnostics methodologies have emerged, with particular interest into machinery operating in non-stationary conditions. In fact continuous speed changes and variable loads make non-trivial the spectrum analysis. A variable speed means a variable characteristic fault frequency related to the damage that is no more recognizable in the spectrum. To overcome this problem the scientific community proposed different approaches listed in two main categories: model-based approaches and expert systems. In this context the paper aims to present a simple expert system derived from the mechanisms of the immune system called Euclidean Distance Minimization, and its application in a real case of bearing faults recognition. The proposed method is a simplification of the original process, adapted by the class of Artificial Immune Systems, which proved to be useful and promising in different application fields. Comparative results are provided, with a complete explanation of the algorithm and its functioning aspects.

Modeling selective attention using a neuromorphic analog VLSI device.

PubMed

Indiveri, G

2000-12-01

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.

Demonstrating artificial intelligence for space systems - Integration and project management issues

NASA Technical Reports Server (NTRS)

Hack, Edmund C.; Difilippo, Denise M.

1990-01-01

As part of its Systems Autonomy Demonstration Project (SADP), NASA has recently demonstrated the Thermal Expert System (TEXSYS). Advanced real-time expert system and human interface technology was successfully developed and integrated with conventional controllers of prototype space hardware to provide intelligent fault detection, isolation, and recovery capability. Many specialized skills were required, and responsibility for the various phases of the project therefore spanned multiple NASA centers, internal departments and contractor organizations. The test environment required communication among many types of hardware and software as well as between many people. The integration, testing, and configuration management tools and methodologies which were applied to the TEXSYS project to assure its safe and successful completion are detailed. The project demonstrated that artificial intelligence technology, including model-based reasoning, is capable of the monitoring and control of a large, complex system in real time.

Detection of rain events in radiological early warning networks with spectro-dosimetric systems

NASA Astrophysics Data System (ADS)

Dąbrowski, R.; Dombrowski, H.; Kessler, P.; Röttger, A.; Neumaier, S.

2017-10-01

Short-term pronounced increases of the ambient dose equivalent rate, due to rainfall are a well-known phenomenon. Increases in the same order of magnitude or even below may also be caused by a nuclear or radiological event, i.e. by artificial radiation. Hence, it is important to be able to identify natural rain events in dosimetric early warning networks and to distinguish them from radiological events. Novel spectrometric systems based on scintillators may be used to differentiate between the two scenarios, because the measured gamma spectra provide significant nuclide-specific information. This paper describes three simple, automatic methods to check whether an dot H*(10) increase is caused by a rain event or by artificial radiation. These methods were applied to measurements of three spectrometric systems based on CeBr3, LaBr3 and SrI2 scintillation crystals, investigated and tested for their practicability at a free-field reference site of PTB.

A Hybrid Multiuser Detector Based on MMSE and AFSA for TDRS System Forward Link

PubMed Central

Yin, Zhendong; Liu, Xiaohui

2014-01-01

This study mainly focuses on multiuser detection in tracking and data relay satellite (TDRS) system forward link. Minimum mean square error (MMSE) is a low complexity multiuser detection method, but MMSE detector cannot achieve satisfactory bit error ratio and near-far resistance, whereas artificial fish swarm algorithm (AFSA) is expert in optimization and it can realize the global convergence efficiently. Therefore, a hybrid multiuser detector based on MMSE and AFSA (MMSE-AFSA) is proposed in this paper. The result of MMSE and its modified formations are used as the initial values of artificial fishes to accelerate the speed of global convergence and reduce the iteration times for AFSA. The simulation results show that the bit error ratio and near-far resistance performances of the proposed detector are much better, compared with MF, DEC, and MMSE, and are quite close to OMD. Furthermore, the proposed MMSE-AFSA detector also has a large system capacity. PMID:24883418

Development and program implementation of elements for identification of the electromagnet condition for movable element position control

NASA Astrophysics Data System (ADS)

Leukhin, R. I.; Shaykhutdinov, D. V.; Shirokov, K. M.; Narakidze, N. D.; Vlasov, A. S.

2017-02-01

Developing the experimental design of new electromagnetic constructions types in engineering industry enterprises requires solutions of two major problems: regulator’s parameters setup and comprehensive testing of electromagnets. A weber-ampere characteristic as a data source for electromagnet condition identification was selected. Present article focuses on development and implementation of the software for electromagnetic drive control system based on the weber-ampere characteristic measuring. The software for weber-ampere characteristic data processing based on artificial neural network is developed. Results of the design have been integrated into the program code in LabVIEW environment. The license package of LabVIEW graphic programming was used. The hardware is chosen and possibility of its use for control system implementation was proved. The trained artificial neural network defines electromagnetic drive effector position with minimal error. Developed system allows to control the electromagnetic drive powered by the voltage source, the current source and hybrid sources.

Localization of source with unknown amplitude using IPMC sensor arrays

NASA Astrophysics Data System (ADS)

Abdulsadda, Ahmad T.; Zhang, Feitian; Tan, Xiaobo

2011-04-01

The lateral line system, consisting of arrays of neuromasts functioning as flow sensors, is an important sensory organ for fish that enables them to detect predators, locate preys, perform rheotaxis, and coordinate schooling. Creating artificial lateral line systems is of significant interest since it will provide a new sensing mechanism for control and coordination of underwater robots and vehicles. In this paper we propose recursive algorithms for localizing a vibrating sphere, also known as a dipole source, based on measurements from an array of flow sensors. A dipole source is frequently used in the study of biological lateral lines, as a surrogate for underwater motion sources such as a flapping fish fin. We first formulate a nonlinear estimation problem based on an analytical model for the dipole-generated flow field. Two algorithms are presented to estimate both the source location and the vibration amplitude, one based on the least squares method and the other based on the Newton-Raphson method. Simulation results show that both methods deliver comparable performance in source localization. A prototype of artificial lateral line system comprising four ionic polymer-metal composite (IPMC) sensors is built, and experimental results are further presented to demonstrate the effectiveness of IPMC lateral line systems and the proposed estimation algorithms.

Implementing embedded artificial intelligence rules within algorithmic programming languages

NASA Technical Reports Server (NTRS)

Feyock, Stefan

1988-01-01

Most integrations of artificial intelligence (AI) capabilities with non-AI (usually FORTRAN-based) application programs require the latter to execute separately to run as a subprogram or, at best, as a coroutine, of the AI system. In many cases, this organization is unacceptable; instead, the requirement is for an AI facility that runs in embedded mode; i.e., is called as subprogram by the application program. The design and implementation of a Prolog-based AI capability that can be invoked in embedded mode are described. The significance of this system is twofold: Provision of Prolog-based symbol-manipulation and deduction facilities makes a powerful symbolic reasoning mechanism available to applications programs written in non-AI languages. The power of the deductive and non-procedural descriptive capabilities of Prolog, which allow the user to describe the problem to be solved, rather than the solution, is to a large extent vitiated by the absence of the standard control structures provided by other languages. Embedding invocations of Prolog rule bases in programs written in non-AI languages makes it possible to put Prolog calls inside DO loops and similar control constructs. The resulting merger of non-AI and AI languages thus results in a symbiotic system in which the advantages of both programming systems are retained, and their deficiencies largely remedied.

Empowered citizen 'health hackers' who are not waiting.

PubMed

Omer, Timothy

2016-08-17

Due to the easier access to information, the availability of low cost technologies and the involvement of well educated, passionate patients, a group of citizen 'Health Hackers', who are building their own medical systems to help them overcome the unmet needs of their conditions, is emerging. This has recently been the case in the type 1 diabetes community, under the movement #WeAreNotWaiting, with innovative use of current medical devices hacked to access data and Open-Source code producing solutions ranging from remote monitoring of diabetic children to producing an Artificial Pancreas System to automate the management and monitoring of a patient's condition. Timothy Omer is working with the community to utilise the technology already in his pocket to build a mobile- and smartwatch-based Artificial Pancreas System.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

An enzymatic assay based on luciferase Ebola virus-like particles for evaluation of virolytic activity of antimicrobial peptides.

PubMed

Peskova, Marie; Heger, Zbynek; Janda, Petr; Adam, Vojtech; Pekarik, Vladimir

2017-02-01

Antimicrobial peptides are currently considered as promising antiviral compounds. Current assays to evaluate the effectivity of peptides against enveloped viruses based on liposomes or hemolysis are encumbered by the artificial nature of liposomes or distinctive membrane composition of used erythrocytes. We propose a novel assay system based on enzymatic Ebola virus-like particles containing sensitive luciferase reporter. The assay was validated with several cationic and anionic peptides and compared with lentivirus inactivation and hemolytic assays. The assay is sensitive and easy to perform in standard biosafety level laboratory with potential for high-throughput screens. The use of virus-like particles in the assay provides a system as closely related to the native viruses as possible eliminating some issues associated with other more artificial set ups. We have identified CAM-W (KWKLWKKIEKWGQGIGAVLKWLTTWL) as a peptide with the greatest antiviral activity against infectious lentiviral vectors and filoviral virus-like particles. Copyright © 2016 Elsevier Inc. All rights reserved.

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

PubMed

Kim, Seongjung; Kim, Jongman; Ahn, Soonjae; Kim, Youngho

2018-04-18

Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Artificial Intelligence and Spacecraft Power Systems

NASA Technical Reports Server (NTRS)

Dugel-Whitehead, Norma R.

1997-01-01

This talk will present the work which has been done at NASA Marshall Space Flight Center involving the use of Artificial Intelligence to control the power system in a spacecraft. The presentation will include a brief history of power system automation, and some basic definitions of the types of artificial intelligence which have been investigated at MSFC for power system automation. A video tape of one of our autonomous power systems using co-operating expert systems, and advanced hardware will be presented.

An ArcGIS decision support tool for artificial reefs site selection (ArcGIS ARSS)

NASA Astrophysics Data System (ADS)

Stylianou, Stavros; Zodiatis, George

2017-04-01

Although the use and benefits of artificial reefs, both socio-economic and environmental, have been recognized with research and national development programmes worldwide their development is rarely subjected to a rigorous site selection process and the majority of the projects use the traditional (non-GIS) approach, based on trial and error mode. Recent studies have shown that the use of Geographic Information Systems, unlike to traditional methods, for the identification of suitable areas for artificial reefs siting seems to offer a number of distinct advantages minimizing possible errors, time and cost. A decision support tool (DSS) has been developed based on the existing knowledge, the multi-criteria decision analysis techniques and the GIS approach used in previous studies in order to help the stakeholders to identify the optimal locations for artificial reefs deployment on the basis of the physical, biological, oceanographic and socio-economic features of the sites. The tool provides to the users the ability to produce a final report with the results and suitability maps. The ArcGIS ARSS support tool runs within the existing ArcMap 10.2.x environment and for the development the VB .NET high level programming language has been used along with ArcObjects 10.2.x. Two local-scale case studies were conducted in order to test the application of the tool focusing on artificial reef siting. The results obtained from the case studies have shown that the tool can be successfully integrated within the site selection process in order to select objectively the optimal site for artificial reefs deployment.

A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems

DTIC Science & Technology

1990-11-01

Intelligence Systems," in Distributed Artifcial Intelligence , vol. II, L. Gasser and M. Huhns (eds), Pitman, London, 1989, pp. 413-430. Shaw, M. Harrow, B...IDTIC FILE COPY A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems N Michael I. Shaw...SUBTITLE 5. FUNDING NUMBERS A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems 6

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

2016-07-05

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

Artificial intelligence-assisted occupational lung disease diagnosis.

PubMed

Harber, P; McCoy, J M; Howard, K; Greer, D; Luo, J

1991-08-01

An artificial intelligence expert-based system for facilitating the clinical recognition of occupational and environmental factors in lung disease has been developed in a pilot fashion. It utilizes a knowledge representation scheme to capture relevant clinical knowledge into structures about specific objects (jobs, diseases, etc) and pairwise relations between objects. Quantifiers describe both the closeness of association and risk, as well as the degree of belief in the validity of a fact. An independent inference engine utilizes the knowledge, combining likelihoods and uncertainties to achieve estimates of likelihood factors for specific paths from work to illness. The system creates a series of "paths," linking work activities to disease outcomes. One path links a single period of work to a single possible disease outcome. In a preliminary trial, the number of "paths" from job to possible disease averaged 18 per subject in a general population and averaged 25 per subject in an asthmatic population. Artificial intelligence methods hold promise in the future to facilitate diagnosis in pulmonary and occupational medicine.

Magnetotransport in Artificial Kagome Spin Ice

NASA Astrophysics Data System (ADS)

Chern, Gia-Wei

2017-12-01

Magnetic nanoarrays with special geometries exhibit nontrivial collective behaviors similar to those observed in spin-ice materials. Here, we present a circuit model to describe the complex magnetotransport phenomena in artificial kagome spin ice. In this picture, the system can be viewed as a resistor network driven by voltage sources that are located at vertices of the honeycomb array. The differential voltages across different terminals of these sources are related to the ice rules that govern the local magnetization ordering. The circuit model relates the transverse Hall voltage of kagome ice to the underlying spin correlations. Treating the magnetic nanoarray as metamaterials, we present a mesoscopic constitutive equation relating the Hall resistance to magnetization components of the system. We further show that the Hall signal is significantly enhanced when the kagome ice undergoes a magnetic-charge-ordering transition. Our analysis can be readily generalized to other lattice geometries, providing a quantitative method for the design of magnetoresistance devices based on artificial spin ice.

Fatty acid profiles of benthic environment associated with artificial reefs in subtropical Hong Kong.

PubMed

Cheung, Siu Gin; Wai, Ho Yin; Shin, Paul K S

2010-02-01

Artificial reefs can enhance habitat heterogeneity, especially in seabed degraded by bottom-dredging and trawling. However, the trophodynamics of such reef systems are not well understood. This study provided baseline data on trophic relationships in the benthic environment associated with artificial reefs in late spring and mid summer of subtropical Hong Kong, using fatty acid profiles as an indicator. Data from sediments collected at the reef base, materials from sediment traps deployed on top and bottom of the reefs, total particulate matter from the water column and oyster tissues from reef surface were subjected to principal component analysis. Results showed variations of fatty acid profiles in the total particulate matter, upper sediment trap and oyster tissue samples collected in the two samplings, indicating seasonal, trophodynamic changes within the reef system. The wastes produced by fish aggregating at the reefs can also contribute a source of biodeposits to the nearby benthic environment. Copyright 2009 Elsevier Ltd. All rights reserved.

Tunnel junction based memristors as artificial synapses

PubMed Central

Thomas, Andy; Niehörster, Stefan; Fabretti, Savio; Shepheard, Norman; Kuschel, Olga; Küpper, Karsten; Wollschläger, Joachim; Krzysteczko, Patryk; Chicca, Elisabetta

2015-01-01

We prepared magnesia, tantalum oxide, and barium titanate based tunnel junction structures and investigated their memristive properties. The low amplitudes of the resistance change in these types of junctions are the major obstacle for their use. Here, we increased the amplitude of the resistance change from 10% up to 100%. Utilizing the memristive properties, we looked into the use of the junction structures as artificial synapses. We observed analogs of long-term potentiation, long-term depression and spike-time dependent plasticity in these simple two terminal devices. Finally, we suggest a possible pathway of these devices toward their integration in neuromorphic systems for storing analog synaptic weights and supporting the implementation of biologically plausible learning mechanisms. PMID:26217173

Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data.

PubMed

Azarkhish, Iman; Raoufy, Mohammad Reza; Gharibzadeh, Shahriar

2012-06-01

Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.

Enabling Autonomous Space Mission Operations with Artificial Intelligence

NASA Technical Reports Server (NTRS)

Frank, Jeremy

2017-01-01

For over 50 years, NASA's crewed missions have been confined to the Earth-Moon system, where speed-of-light communications delays between crew and ground are practically nonexistent. This ground-centered mode of operations, with a large, ground-based support team, is not sustainable for NASAs future human exploration missions to Mars. Future astronauts will need smarter tools employing Artificial Intelligence (AI) techniques make decisions without inefficient communication back and forth with ground-based mission control. In this talk we will describe several demonstrations of astronaut decision support tools using AI techniques as a foundation. These demonstrations show that astronauts tasks ranging from living and working to piloting can benefit from AI technology development.

A wet-tolerant adhesive patch inspired by protuberances in suction cups of octopi

NASA Astrophysics Data System (ADS)

Baik, Sangyul; Kim, Da Wan; Park, Youngjin; Lee, Tae-Jin; Ho Bhang, Suk; Pang, Changhyun

2017-06-01

Adhesion strategies that rely on mechanical interlocking or molecular attractions between surfaces can suffer when coming into contact with liquids. Thus far, artificial wet and dry adhesives have included hierarchical mushroom-shaped or porous structures that allow suction or capillarity, supramolecular structures comprising nanoparticles, and chemistry-based attractants that use various protein polyelectrolytes. However, it is challenging to develop adhesives that are simple to make and also perform well—and repeatedly—under both wet and dry conditions, while avoiding non-chemical contamination on the adhered surfaces. Here we present an artificial, biologically inspired, reversible wet/dry adhesion system that is based on the dome-like protuberances found in the suction cups of octopi. To mimic the architecture of these protuberances, we use a simple, solution-based, air-trap technique that involves fabricating a patterned structure as a polymeric master, and using it to produce a reversed architecture, without any sophisticated chemical syntheses or surface modifications. The micrometre-scale domes in our artificial adhesive enhance the suction stress. This octopus-inspired system exhibits strong, reversible, highly repeatable adhesion to silicon wafers, glass, and rough skin surfaces under various conditions (dry, moist, under water and under oil). To demonstrate a potential application, we also used our adhesive to transport a large silicon wafer in air and under water without any resulting surface contamination.

Automatic Clustering of Rolling Element Bearings Defects with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Antonini, M.; Faglia, R.; Pedersoli, M.; Tiboni, M.

2006-06-01

The paper presents the optimization of a methodology for automatic clustering based on Artificial Neural Networks to detect the presence of defects in rolling bearings. The research activity was developed in co-operation with an Italian company which is expert in the production of water pumps for automotive use (Industrie Saleri Italo). The final goal of the work is to develop a system for the automatic control of the pumps, at the end of the production line. In this viewpoint, we are gradually considering the main elements of the water pump, which can cause malfunctioning. The first elements we have considered are the rolling bearing, a very critic component for the system. The experimental activity is based on the vibration measuring of rolling bearings opportunely damaged; vibration signals are in the second phase elaborated; the third and last phase is an automatic clustering. Different signal elaboration techniques are compared to optimize the methodology.

Stress-based control of magnetic nanowire domain walls in artificial multiferroic systems

NASA Astrophysics Data System (ADS)

Dean, J.; Bryan, M. T.; Schrefl, T.; Allwood, D. A.

2011-01-01

Artificial multiferroic systems, which combine piezoelectric and piezomagnetic materials, offer novel methods of controlling material properties. Here, we use combined structural and magnetic finite element models to show how localized strains in a piezoelectric film coupled to a piezomagnetic nanowire can attract and pin magnetic domain walls. Synchronous switching of addressable contacts enables the controlled movement of pinning sites, and hence domain walls, in the nanowire without applied magnetic field or spin-polarized current, irrespective of domain wall structure. Conversely, domain wall-induced strain in the piezomagnetic material induces a local potential difference in the piezoelectric, providing a mechanism for sensing domain walls. This approach overcomes the problems in magnetic nanowire memories of domain wall structure-dependent behavior and high power consumption. Nonvolatile random access or shift register memories based on these effects can achieve storage densities >1 Gbit/In2, sub-10 ns switching times, and power consumption <100 keV per operation.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

PubMed

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

PubMed Central

Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

Using artificial intelligence to automate remittance processing.

PubMed

Adams, W T; Snow, G M; Helmick, P M

1998-06-01

The consolidated business office of the Allegheny Health Education Research Foundation (AHERF), a large integrated healthcare system based in Pittsburgh, Pennsylvania, sought to improve its cash-related business office activities by implementing an automated remittance processing system that uses artificial intelligence. The goal was to create a completely automated system whereby all monies it processed would be tracked, automatically posted, analyzed, monitored, controlled, and reconciled through a central database. Using a phased approach, the automated payment system has become the central repository for all of the remittances for seven of the hospitals in the AHERF system and has allowed for the complete integration of these hospitals' existing billing systems, document imaging system, and intranet, as well as the new automated payment posting, and electronic cash tracking and reconciling systems. For such new technology, which is designed to bring about major change, factors contributing to the project's success were adequate planning, clearly articulated objectives, marketing, end-user acceptance, and post-implementation plan revision.

Methods and decision making on a Mars rover for identification of fossils

NASA Technical Reports Server (NTRS)

Eberlein, Susan; Yates, Gigi

1989-01-01

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

PubMed

Sun, Liping; Luo, Yonglong; Ding, Xintao; Zhang, Ji

2014-01-01

An important component of a spatial clustering algorithm is the distance measure between sample points in object space. In this paper, the traditional Euclidean distance measure is replaced with innovative obstacle distance measure for spatial clustering under obstacle constraints. Firstly, we present a path searching algorithm to approximate the obstacle distance between two points for dealing with obstacles and facilitators. Taking obstacle distance as similarity metric, we subsequently propose the artificial immune clustering with obstacle entity (AICOE) algorithm for clustering spatial point data in the presence of obstacles and facilitators. Finally, the paper presents a comparative analysis of AICOE algorithm and the classical clustering algorithms. Our clustering model based on artificial immune system is also applied to the case of public facility location problem in order to establish the practical applicability of our approach. By using the clone selection principle and updating the cluster centers based on the elite antibodies, the AICOE algorithm is able to achieve the global optimum and better clustering effect.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Micro-optical artificial compound eyes.

PubMed

Duparré, J W; Wippermann, F C

2006-03-01

Natural compound eyes combine small eye volumes with a large field of view at the cost of comparatively low spatial resolution. For small invertebrates such as flies or moths, compound eyes are the perfectly adapted solution to obtaining sufficient visual information about their environment without overloading their brains with the necessary image processing. However, to date little effort has been made to adopt this principle in optics. Classical imaging always had its archetype in natural single aperture eyes which, for example, human vision is based on. But a high-resolution image is not always required. Often the focus is on very compact, robust and cheap vision systems. The main question is consequently: what is the better approach for extremely miniaturized imaging systems-just scaling of classical lens designs or being inspired by alternative imaging principles evolved by nature in the case of small insects? In this paper, it is shown that such optical systems can be achieved using state-of-the-art micro-optics technology. This enables the generation of highly precise and uniform microlens arrays and their accurate alignment to the subsequent optics-, spacing- and optoelectronics structures. The results are thin, simple and monolithic imaging devices with a high accuracy of photolithography. Two different artificial compound eye concepts for compact vision systems have been investigated in detail: the artificial apposition compound eye and the cluster eye. Novel optical design methods and characterization tools were developed to allow the layout and experimental testing of the planar micro-optical imaging systems, which were fabricated for the first time by micro-optics technology. The artificial apposition compound eye can be considered as a simple imaging optical sensor while the cluster eye is capable of becoming a valid alternative to classical bulk objectives but is much more complex than the first system.

Space station data system analysis/architecture study. Task 5: Program plan

NASA Technical Reports Server (NTRS)

1985-01-01

Cost estimates for both the on-board and ground segments of the Space Station Data System (SSDS) are presented along with summary program schedules. Advanced technology development recommendations are provided in the areas of distributed data base management, end-to-end protocols, command/resource management, and flight qualified artificial intelligence machines.

Development of a Knowledge Base for Use in an Expert System Advisor for Aircraft Maintenance Scheduling (ESAAMS)

DTIC Science & Technology

1991-03-01

Cliffs, New Jersey, 1989. Merritt, Dennis, "Forward Chaining in Prolog," Al Expert, v.7 November 1986. Minsky , Marvin ., "A Framework for Representing... Minsky , Marvin , (editor), Semantic Information Processing, MIT Press, 1968. Rychener, M. D., Production Systems as a Programming Language for Artificial

Using Students' Knowledge to Generate Individual Feedback: Concept for an Intelligent Educational System on Logistics.

ERIC Educational Resources Information Center

Ziems, Dietrich; Neumann, Gaby

1997-01-01

Discusses a methods kit for interactive problem-solving exercises in engineering education as well as a methodology for intelligent evaluation of solutions. The quality of a system teaching logistics thinking can be improved using artificial intelligence. Embedding a rule-based diagnosis module that evaluates the student's knowledge actively…

First CLIPS Conference Proceedings, volume 2

NASA Technical Reports Server (NTRS)

1990-01-01

The topics of volume 2 of First CLIPS Conference are associated with following applications: quality control; intelligent data bases and networks; Space Station Freedom; Space Shuttle and satellite; user interface; artificial neural systems and fuzzy logic; parallel and distributed processing; enchancements to CLIPS; aerospace; simulation and defense; advisory systems and tutors; and intelligent control.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Applications of Artificial Intelligence to Information Search and Retrieval: The Development and Testing of an Intelligent Technical Information System.

ERIC Educational Resources Information Center

Harvey, Francis A.

This paper describes the evolution and development of an intelligent information system, i.e., a knowledge base for steel structures being undertaken as part of the Technical Information Center for Steel Structures at Lehigh University's Center of Advanced Technology for Large Structural Systems (ATLSS). The initial development of the Technical…

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

PubMed Central

Gonzalez, Luis F.; Montes, Glen A.; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J.

2016-01-01

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification. PMID:26784196

A spatiotemporal structure: common to subatomic systems, biological processes, and economic cycles

NASA Astrophysics Data System (ADS)

Naitoh, Ken

2012-03-01

A theoretical model derived based on a quasi-stability concept applied to momentum conservation (Naitoh, JJIAM, 2001, Artificial Life Robotics, 2008, 2010) has revealed the spatial structure of various systems. This model explains the reason why particles such as biological cells, nitrogenous bases, and liquid droplets have bimodal size ratios of about 2:3 and 1:1. This paper shows that the same theory holds true for several levels of parcels from baryons to stars in the cosmos: specifically, at the levels of nuclear force, van der Waals force, surface tension, and the force of gravity. A higher order of analysis clarifies other asymmetric ratios related to the halo structure seen in atoms and amino acids. We will also show that our minimum hypercycle theory for explaining the morphogenetic cycle (Naitoh, Artificial Life Robotics, 2008) reveals other temporal cycles such as those of economic systems and the circadian clock as well as the fundamental neural network pattern (topological pattern). Finally, a universal equation describing the spatiotemporal structure of several systems will be derived, which also leads to a general concept of quasi-stability.

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation.

PubMed

Gonzalez, Luis F; Montes, Glen A; Puig, Eduard; Johnson, Sandra; Mengersen, Kerrie; Gaston, Kevin J

2016-01-14

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Classifying Sources Influencing Indoor Air Quality (IAQ) Using Artificial Neural Network (ANN)

PubMed Central

Mad Saad, Shaharil; Melvin Andrew, Allan; Md Shakaff, Ali Yeon; Mohd Saad, Abdul Rahman; Muhamad Yusof @ Kamarudin, Azman; Zakaria, Ammar

2015-01-01

Monitoring indoor air quality (IAQ) is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC), base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity. PMID:26007724

Adaptation of a Knowledge-Based Decision-Support System in the Tactical Environment.

DTIC Science & Technology

1981-12-01

002-04-6411S1CURITY CL All PICATION OF 1,416 PAGE (00HIR Onto ea0aOW .L10 *GU9WVC 4bGSI.CAYON S. Voss 10466lVka t... OftesoE ’ making decisons . The...noe..aaw Ad tdlalttt’ IV 680011 MMib) Artificial Intelligence; Decision-Support Systems; Tactical Decision- making ; Knowledge-based Decision-support...tactical information to assist tactical commanders in making decisions. The system, TAC*, for "Tactical Adaptable Consultant," incorporates a database

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

PubMed Central

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2013-01-01

Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. PMID:21917512

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

eFurniture for home-based frailty detection using artificial neural networks and wireless sensors.

PubMed

Chang, Yu-Chuan; Lin, Chung-Chih; Lin, Pei-Hsin; Chen, Chun-Chang; Lee, Ren-Guey; Huang, Jing-Siang; Tsai, Tsai-Hsuan

2013-02-01

The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty. Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

Autonomously generating operations sequences for a Mars Rover using AI-based planning

NASA Technical Reports Server (NTRS)

Sherwood, Rob; Mishkin, Andrew; Estlin, Tara; Chien, Steve; Backes, Paul; Cooper, Brian; Maxwell, Scott; Rabideau, Gregg

2001-01-01

This paper discusses a proof-of-concept prototype for ground-based automatic generation of validated rover command sequences from highlevel science and engineering activities. This prototype is based on ASPEN, the Automated Scheduling and Planning Environment. This Artificial Intelligence (AI) based planning and scheduling system will automatically generate a command sequence that will execute within resource constraints and satisfy flight rules.

Continuous Glucose Monitoring

MedlinePlus

... costs will be covered. What is an artificial pancreas? A CGM is one part of the “artificial pancreas” systems that are beginning to reach people with ... has played an important role in developing artificial pancreas technology. An artificial pancreas replaces manual blood glucose ...

MESA: An Interactive Modeling and Simulation Environment for Intelligent Systems Automation

NASA Technical Reports Server (NTRS)

Charest, Leonard

1994-01-01

This report describes MESA, a software environment for creating applications that automate NASA mission opterations. MESA enables intelligent automation by utilizing model-based reasoning techniques developed in the field of Artificial Intelligence. Model-based reasoning techniques are realized in Mesa through native support of causal modeling and discrete event simulation.

Organic-based tristimuli colorimeter

NASA Astrophysics Data System (ADS)

Antognazza, M. R.; Scherf, U.; Monti, P.; Lanzani, G.

2007-04-01

The authors realize three photodiodes based on organic materials, which have photoresponse curves matched to the colorimetric functions of the standard observer. Such a system of detectors is used for realizing a three-stimuli colorimeter. They report the result of measurements in different spectral areas and suggest possible application of the device in color science and artificial vision.

MENO-II: An AI-Based Programming Tutor.

ERIC Educational Resources Information Center

Soloway, Elliot; And Others

This report examines the features and performance of the BUG-FINDing component of MENO-II, a computer-based tutor for beginning PASCAL programming students. A discussion of the use of artificial intelligence techniques is followed by a summary of the system status and objectives. The two main components of MENO-II are described, beginning with the…

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

PubMed Central

Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

2016-01-01

In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. PMID:27123002

Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

ERIC Educational Resources Information Center

Buckland, Michael K.; Florian, Doris

1991-01-01

Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

Blood feeding of Ornithodoros turicata larvae using an artificial membrane system

USDA-ARS?s Scientific Manuscript database

An artificial membrane system was adapted to feed Ornithodoros turicata larvae from a laboratory colony using defibrinated swine blood. Aspects related to larval feeding and molting to the 1st nymphal instar were evaluated. Fifty-five percent of all larvae exposed to the artificial membrane in two e...

Mass production of bulk artificial nacre with excellent mechanical properties.

PubMed

Gao, Huai-Ling; Chen, Si-Ming; Mao, Li-Bo; Song, Zhao-Qiang; Yao, Hong-Bin; Cölfen, Helmut; Luo, Xi-Sheng; Zhang, Fu; Pan, Zhao; Meng, Yu-Feng; Ni, Yong; Yu, Shu-Hong

2017-08-18

Various methods have been exploited to replicate nacre features into artificial structural materials with impressive structural and mechanical similarity. However, it is still very challenging to produce nacre-mimetics in three-dimensional bulk form, especially for further scale-up. Herein, we demonstrate that large-sized, three-dimensional bulk artificial nacre with comprehensive mimicry of the hierarchical structures and the toughening mechanisms of natural nacre can be facilely fabricated via a bottom-up assembly process based on laminating pre-fabricated two-dimensional nacre-mimetic films. By optimizing the hierarchical architecture from molecular level to macroscopic level, the mechanical performance of the artificial nacre is superior to that of natural nacre and many engineering materials. This bottom-up strategy has no size restriction or fundamental barrier for further scale-up, and can be easily extended to other material systems, opening an avenue for mass production of high-performance bulk nacre-mimetic structural materials in an efficient and cost-effective way for practical applications.Artificial materials that replicate the mechanical properties of nacre represent important structural materials, but are difficult to produce in bulk. Here, the authors exploit the bottom-up assembly of 2D nacre-mimetic films to fabricate 3D bulk artificial nacre with an optimized architecture and excellent mechanical properties.

Autonomous Driver Based on an Intelligent System of Decision-Making.

PubMed

Czubenko, Michał; Kowalczuk, Zdzisław; Ordys, Andrew

The paper presents and discusses a system ( xDriver ) which uses an Intelligent System of Decision-making (ISD) for the task of car driving. The principal subject is the implementation, simulation and testing of the ISD system described earlier in our publications (Kowalczuk and Czubenko in artificial intelligence and soft computing lecture notes in computer science, lecture notes in artificial intelligence, Springer, Berlin, 2010, 2010, In Int J Appl Math Comput Sci 21(4):621-635, 2011, In Pomiary Autom Robot 2(17):60-5, 2013) for the task of autonomous driving. The design of the whole ISD system is a result of a thorough modelling of human psychology based on an extensive literature study. Concepts somehow similar to the ISD system can be found in the literature (Muhlestein in Cognit Comput 5(1):99-105, 2012; Wiggins in Cognit Comput 4(3):306-319, 2012), but there are no reports of a system which would model the human psychology for the purpose of autonomously driving a car. The paper describes assumptions for simulation, the set of needs and reactions (characterizing the ISD system), the road model and the vehicle model, as well as presents some results of simulation. It proves that the xDriver system may behave on the road as a very inexperienced driver.

Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

PubMed

Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

2015-01-01

Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

Design of a hydraulic analog of the circulatory system for evaluating artificial hearts.

PubMed

Donovan, F M

1975-01-01

A major problem in improving artificial heart designs is the absence of methods for accurate in vitro testing of artificial heart systems. A mock circulatory system has been constructed which hydraulically simulates the systemic and pulmonary circulations of the normal human. The device is constructed of 1/2 in. acrylic sheet and has overall dimensions of 24 in. wide, 16 in. tall, and 8 in. deep. The artificial heart to be tested is attached to the front of the device, and pumps fluid from the systemic venous chamber into the pulmonary arterial chamber and from the pulmonary venous chamber into the systemic arterial chamber. Each of the four chambers is hermetically sealed. The compliance of each chamber is determined by the volume of air trapped above the fluid in that chamber. The pulmonary and systemic resistances are set automatically by bellows-operated valves to simulate the barroreceptor response in the systemic arteries and the passive pulmonary resistance response in the pulmonary arteries. Cardiac output is measured by a turbine flowmeter in the systemic circulation. Results using the Kwan-Gett artificial heart show a good comparison between the mock circulatory system response and the calf response.

Neuromorphic Computing for Very Large Test and Evaluation Data Analysis

DTIC Science & Technology

2014-05-01

analysis and utilization of newly available hardware- based artificial neural network chips. These two aspects of the program are complementary. The...neuromorphic architectures research focused on long term disruptive technologies with high risk but revolutionary potential. The hardware- based neural...today. Overall, hardware- based neural processing research allows us to study the fundamental system and architectural issues relevant for employing

Reflexive reasoning for distributed real-time systems

NASA Technical Reports Server (NTRS)

Goldstein, David

1994-01-01

This paper discusses the implementation and use of reflexive reasoning in real-time, distributed knowledge-based applications. Recently there has been a great deal of interest in agent-oriented systems. Implementing such systems implies a mechanism for sharing knowledge, goals and other state information among the agents. Our techniques facilitate an agent examining both state information about other agents and the parameters of the knowledge-based system shell implementing its reasoning algorithms. The shell implementing the reasoning is the Distributed Artificial Intelligence Toolkit, which is a derivative of CLIPS.

Modeling of dielectric elastomer oscillators for soft biomimetic applications.

PubMed

Henke, E-F M; Wilson, Katherine E; Anderson, I A

2018-06-26

Biomimetic, entirely soft robots with animal-like behavior and integrated artificial nervous systems will open up totally new perspectives and applications. However, until now, most presented studies on soft robots were limited to only partly soft designs, since all solutions at least needed conventional, stiff electronics to sense, process signals and activate actuators. We present a novel approach for a set up and the experimental validation of an artificial pace maker that is able to drive basic robotic structures and act as artificial central pattern generator. The structure is based on multi-functional dielectric elastomers (DEs). DE actuators, DE switches and DE resistors are combined to create complex DE oscillators (DEOs). Supplied with only one external DC voltage, the DEO autonomously generates oscillating signals that can be used to clock a robotic structure, control the cyclic motion of artificial muscles in bionic robots or make a whole robotic structure move. We present the basic functionality, derive a mathematical model for predicting the generated signal waveform and verify the model experimentally.

Artificial consciousness and the consciousness-attention dissociation.

PubMed

Haladjian, Harry Haroutioun; Montemayor, Carlos

2016-10-01

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems-these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness. Copyright © 2016 Elsevier Inc. All rights reserved.

Bacterial fermentation platform for producing artificial aromatic amines

PubMed Central

Masuo, Shunsuke; Zhou, Shengmin; Kaneko, Tatsuo; Takaya, Naoki

2016-01-01

Aromatic amines containing an aminobenzene or an aniline moiety comprise versatile natural and artificial compounds including bioactive molecules and resources for advanced materials. However, a bio-production platform has not been implemented. Here we constructed a bacterial platform for para-substituted aminobenzene relatives of aromatic amines via enzymes in an alternate shikimate pathway predicted in a Pseudomonad bacterium. Optimization of the metabolic pathway in Escherichia coli cells converted biomass glucose to 4-aminophenylalanine with high efficiency (4.4 g L−1 in fed-batch cultivation). We designed and produced artificial pathways that mimicked the fungal Ehrlich pathway in E. coli and converted 4-aminophenylalanine into 4-aminophenylethanol and 4-aminophenylacetate at 90% molar yields. Combining these conversion systems or fungal phenylalanine decarboxylases, the 4-aminophenylalanine-producing platform fermented glucose to 4-aminophenylethanol, 4-aminophenylacetate, and 4-phenylethylamine. This original bacterial platform for producing artificial aromatic amines highlights their potential as heteroatoms containing bio-based materials that can replace those derived from petroleum. PMID:27167511

Gain assisted coherent control of microwave pulse in a one dimensional array of artificial atoms

NASA Astrophysics Data System (ADS)

Waqas, Mohsin; Ayaz, M. Q.; Waseem, M.; Qamar, Sajid; Qamar, Shahid

2018-06-01

We study the coherent propagation of a microwave pulse through a one-dimensional array of artificial atoms. The scheme is based upon gain assisted propagation of the pulse using two-photon Raman transition in a three-level superconducting artificial atoms (SAAs) coupled to a microwave transmission line. Our results show that the group velocity can be significantly reduced by increasing the Rabi frequency of the pump fields which in turn can lead to an efficient storage of the pulse inside a 1D array of SAAs. Further, the intensity of the transmitted pulse increases with the number of artificial atoms owing to the gain associated with the two-photon Raman transition. Our results also show that the window width decreases for both scattering and negligible scattering cases with the increase in the number of SAAs. The fidelity of the system also remains high even after the passage of the pulse through a large number of SAAs.

Artificial sweeteners: Sucralose and Acesulfame-K; emerging pollutants indicators of specific transport in karst systems: Application to semi-arid regions.

NASA Astrophysics Data System (ADS)

Doummar, J.; Aoun, M. E.

2016-12-01

The assessment of vulnerability in karst system is highly challenging as it highly depends on the identification of diffuse and concentrated infiltration from surface karst features, the latter varying with time. This work shows that the artificial sweeteners Sucralose (SUC) and Acesulfame-K (ACE-K) can be used as transport indicators for persistent waste water effluent in karst systems. In the framework of a PEER NSF/USAID funded project, a karst spring (Qachqouch; yearly discharge of circa 50 Mm3) in Mount Lebanon was monitored following a multi-precipitation event. Acesulfame-K (ACE-K) and Sucralose (SUC), indicative of domestic waste water, Chloride and Calcium ions, Electrical conductivity (EC), and turbidity along with discharge were collected at the spring for nine days after the rain events at time intervals varying between 30 min and 4 hours. The samples were analyzed for ACE-K and SUC at Eaton Eurofins in California. A first sampling campaign was undertaken to assess the base flow concentrations in wells, waste water, and the main losing river on the catchment area along with Spring Qachqouch. It revealed that ACE-K and SUC are found in the spring at base flow concentrations of 170 ng/l, and 120 ng/l respectively. A two-end mixing model based on Chloride and Calcium allowed the quantification of newly recharged water (11% of total flow) reaching the springs at specific monitoring intervals through fast flow pathways. The results revealed that the artificial sweeteners are persistent in the system outside rain events. They are mostly transported to the springs through fast pathways from flushed wastewater; their breakthrough curves coincide with the arrival of new waters (from the sinking stream or sinkholes). About 120 grams of SUC and 160 grams of ACE-K reached the springs during peak response in three consecutive events. Based on the quantities, and given that both acesulfame and SUC are excreted mostly unchanged in wastewater, estimates of consumption of SUC and ACE-K on the catchment area can be determined accordingly . Moreover, the comparison of breakthrough curves of both artificial sweeteners shows that ACE-K displays a higher variance (translated by a higher longitudinal dispersivity) with respect to SUC.

An improved non-uniformity correction algorithm and its hardware implementation on FPGA

NASA Astrophysics Data System (ADS)

Rong, Shenghui; Zhou, Huixin; Wen, Zhigang; Qin, Hanlin; Qian, Kun; Cheng, Kuanhong

2017-09-01

The Non-uniformity of Infrared Focal Plane Arrays (IRFPA) severely degrades the infrared image quality. An effective non-uniformity correction (NUC) algorithm is necessary for an IRFPA imaging and application system. However traditional scene-based NUC algorithm suffers the image blurring and artificial ghosting. In addition, few effective hardware platforms have been proposed to implement corresponding NUC algorithms. Thus, this paper proposed an improved neural-network based NUC algorithm by the guided image filter and the projection-based motion detection algorithm. First, the guided image filter is utilized to achieve the accurate desired image to decrease the artificial ghosting. Then a projection-based moving detection algorithm is utilized to determine whether the correction coefficients should be updated or not. In this way the problem of image blurring can be overcome. At last, an FPGA-based hardware design is introduced to realize the proposed NUC algorithm. A real and a simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. Experimental results indicated that the proposed NUC algorithm can effectively eliminate the fix pattern noise with less image blurring and artificial ghosting. The proposed hardware design takes less logic elements in FPGA and spends less clock cycles to process one frame of image.

Incomplete response to artificial tears is associated with features of neuropathic ocular pain.

PubMed

Galor, Anat; Batawi, Hatim; Felix, Elizabeth R; Margolis, Todd P; Sarantopoulos, Konstantinos D; Martin, Eden R; Levitt, Roy C

2016-06-01

Artificial tears are first-line therapy for patients with dry eye symptoms. It is not known, however, which patient factors associate with a positive response to therapy. The purpose of this study was to evaluate whether certain ocular and systemic findings are associated with a differential subjective response to artificial tears. Cross-sectional study of 118 individuals reporting artificial tears use (hypromellose 0.4%) to treat dry eye-associated ocular pain. An evaluation was performed to assess dry eye symptoms (via the dry eye questionnaire 5 and ocular surface disease index), ocular and systemic (non-ocular) pain complaints and ocular signs (tear osmolarity, tear breakup time, corneal staining, Schirmer testing with anaesthesia, and eyelid and meibomian gland assessment). The main outcome measures were factors associated with differential subjective response to artificial tears. By self-report, 23 patients reported no improvement, 73 partial improvement and 22 complete improvement in ocular pain with artificial tears. Patients who reported no or partial improvement in pain with artificial tears reported higher levels of hot-burning ocular pain and sensitivity to wind compared with those with complete improvement. Patients were also asked to rate the intensity of systemic pain elsewhere in the body (other than the eye). Patients who reported no or incomplete improvement with artificial tears had higher systemic pain scores compared with those with complete improvement. Both ocular and systemic (non-ocular) pain complaints are associated with a differential subjective response to artificial tears. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Impacts of Artificial Reefs on Surrounding Ecosystems

NASA Astrophysics Data System (ADS)

Manoukian, Sarine

Artificial reefs are becoming a popular biological and management component in shallow water environments characterized by soft seabed, representing both important marine habitats and tools to manage coastal fisheries and resources. An artificial reef in the marine environment acts as an open system with exchange of material and energy, altering the physical and biological characteristics of the surrounding area. Reef stability will depend on the balance of scour, settlement, and burial resulting from ocean conditions over time. Because of the unstable nature of sediments, they require a detailed and systematic investigation. Acoustic systems like high-frequency multibeam sonar are efficient tools in monitoring the environmental evolution around artificial reefs, whereas water turbidity can limit visual dive and ROV inspections. A high-frequency multibeam echo sounder offers the potential of detecting fine-scale distribution of reef units, providing an unprecedented level of resolution, coverage, and spatial definition. How do artificial reefs change over time in relation to the coastal processes? How accurately does multibeam technology map different typologies of artificial modules of known size and shape? How do artificial reefs affect fish school behavior? What are the limitations of multibeam technology for investigating fish school distribution as well as spatial and temporal changes? This study addresses the above questions and presents results of a new approach for artificial reef seafloor mapping over time, based upon an integrated analysis of multibeam swath bathymetry data and geoscientific information (backscatter data analysis, SCUBA observations, physical oceanographic data, and previous findings on the geology and sedimentation processes, integrated with unpublished data) from Senigallia artificial reef, northwestern Adriatic Sea (Italy) and St. Petersburg Beach Reef, west-central Florida continental shelf. A new approach for observation of fish aggregations associated with Senigallia reef based on the analysis of multibeam backscatter data in the water column is also explored. The settlement of the reefs and any terrain change are investigated over time providing a useful description of the local hydrodynamics and geological processes. All the artificial structures (made up by water-based concrete for Senigallia reef and mainly steel for St. Petersburg Beach reef) are identified and those showing substantial horizontal and/or vertical movements are analyzed in detail. Most artificial modules of Senigallia reef are not intact and scour signatures are well depicted around them, indicating reversals of the local current. This is due to both the wind pattern and to the quite close arrangement of the reef units that tend to deflect the bottom flow. As regards to the St. Petersburg Beach reef, all the man-made steel units are still in their upright position. Only a large barge shows a gradual collapse of its south side, and presents well-developed scouring at its east-northeast side, indicating dominant bottom flow from west-southwest to east-northeast. While an overall seafloor depth shallowing of about 0.30 m from down-current deposits was observed for Senigallia reef, an overall deepening of about 0.08 m due to scour was observed at the St. Petersburg Beach reef. Based on the backscatter data interpretation, surficial sediments are coarser in the vicinities of both artificial reefs than corresponding surrounding sediments. Scouring reveals this coarser layer underneath the prevalent mud sediment at Senigallia reef, and the predominant silt sediment at St. Petersburg Beach reef. In the ten years of Senigalia reef study, large-scale variations between clay and silt appear to be directly linked to large flood events that have occurred just prior to the change. As regards the water column investigation, acoustic backscatter from fish aggregations gives detailed information on their morphology and spatial distribution. In addition, relative fish biomass estimates can be extrapolated. Results suggest that most of the fish aggregations are generally associated with the artificial modules showing a tendency for mid- and bottom-water depth distribution than for the surface waters. This study contributes to understanding the changes in artificial reefs over time in relation to coastal processes. Moreover, the preliminary results concerning the water column backscatter data represents progress in fisheries acoustics research as a result of three-dimensional acoustics. They demonstrate the benefits of multibeam sonar as a tool to investigate and quantify size distribution and geometry of fish aggregations associated with shallow marine habitats.

Study on digital teeth selection and virtual teeth arrangement for complete denture.

PubMed

Yu, Xiaoling; Cheng, Xiaosheng; Dai, Ning; Chen, Hu; Yu, Changjiang; Sun, Yuchun

2018-03-01

In dentistry, the complete denture is a conventional treatment for edentulous patients. The computer-aided design and computer-aided manufacturing (CAD/CAM) has been applied on the digital complete denture which is developed rapidly. Tooth selection and arrangement is one of the most important parts in digital complete denture. In this paper, we propose a new method of personalized teeth arrangement. This paper presents a method of arranging teeth virtually for a complete denture. First, scan and extract the feature points of the 3D triangular mesh data of artificial teeth (PLY format), then establish a tooth selection system. Second, scan and mark the anatomic characteristics of the maxillary and mandibular cast surfaces, such as facial midline, the curve of the arches. With the enter information, the study calculates the common arrangement lines of artificial teeth. Third, select the preferred artificial teeth and automatically arrange them virtually in the correct position by using our own software. After that, design the gingival part of the dentures on the basic of the arranged teeth on the screen and then fabricated it by using Computerized Numerical Control (CNC) technology, Rapid Prototyping (RP) technology or 3D printer technology. Finally, select artificial teeth were embedded in wax rims. This system can choose artificial teeth reasonably and the teeth placement can meet the dentist's request to a certain extent, whereas all the operations are based on the medical principles. The study performed here involves computer sciences, medicine, and dentistry, a teeth selection system was proposed and virtual teeth arrangement was described. This study has the capacity of helping operators to select teeth, which improved the accuracy of tooth arrangement, and customized complete denture. Copyright © 2017 Elsevier B.V. All rights reserved.

Virtual instrument: remote control and monitoring of an artificial heart driver

NASA Astrophysics Data System (ADS)

Nguyen, An H.; Farrar, David

1993-07-01

A development of a virtual instrument based on the top-down model approach for an artificial heart driver is presented. Driver parameters and status were being dynamically updated on the virtual system at the remote station. The virtual system allowed the remote operator to interact with the physical heart driver as if he/she were at the local station. Besides use as an effective training tool, the system permits an expert operator to monitor and also control the Thoratec heart driver from a distant location. We believe that the virtual instrument for biomedical devices in general and for the Thoratec heart driver in particular, not only improves system reliability but also opens up a real possibility in reducing medical cost. Utilizing the top-down scheme developed recently for telerobotics, realtime operation in both instrument display and remote communication were possible via a low bandwidth telephone medium.

A toxicity test in artificial soil based on the life-history strategy of the nematode Plectus acuminatus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kammenga, J.E.; Van Koert, P.H.G.; Riksen, J.A.G.

1996-05-01

The ecological risk assessment of toxicants in soil requires reproducible and relevant test systems using a wide range of species. To supplement present test methods from the Organisation of Economic Cooperation and Development (OECD) in artificial soil with earthworms and springtails, a toxicity test in OECD artificial soil has been developed using the bacterivorous nematode Plectus acuminatus (Bastian, 1865) (Nematoda; Plectidae). The juvenile to adult ratio was used as a test parameter since previous life-cycle studies pointed out that fitness of P. acuminatus was strongly determined by changes in both reproduction and juvenile survival. Optimal conditions for the performance ofmore » nematodes in OECD artificial soil were determined (pH{sub KCl} = 5.5, temperature = 20C, and a moisture content of 70% dry wt. artificial soil), and tests were conducted with cadmium, copper, and pentachlorophenol. After an exposure period of 3 weeks the EC50 for cadmium was 321.0 {+-} 1.7 mg/kg dry wt., and the no-observed-effect concentration (NOEC) was 32 mg/kg dry wt. The EC50 for pentachlorophenol was 47.9 {+-} 1.2 mg/kg dry wt., and the NOEC was <10 mg/kg dry wt. For copper the EC50 was 162 {+-} 0.2 mg/kg dry wt., and the NOEC was 32 mg/kg dry wt. It is concluded that the nematode test may well supplement current coil test systems using earthworms and springtails.« less

Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

NASA Technical Reports Server (NTRS)

Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

1989-01-01

This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

Artificial Neural Network Approach in Laboratory Test Reporting:  Learning Algorithms.

PubMed

Demirci, Ferhat; Akan, Pinar; Kume, Tuncay; Sisman, Ali Riza; Erbayraktar, Zubeyde; Sevinc, Suleyman

2016-08-01

In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently. The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method. Data were received from Dokuz Eylül University Central Laboratory. "Training sets" were developed for our experimental model to teach the evaluation criteria. After training the system, "test sets" developed for different conditions were used to statistically assess the validity of the model. After developing the decision algorithm with three iterations of training, no result was verified that was refused by the laboratory specialist. The sensitivity of the model was 91% and specificity was 100%. The estimated κ score was 0.950. This is the first study based on an artificial neural network to build an experimental assessment and decision algorithm model. By integrating our trained algorithm model into a laboratory information system, it may be possible to reduce employees' workload without compromising patient safety. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Development of a coupled expert system for the spacecraft attitude control problem

NASA Technical Reports Server (NTRS)

Kawamura, K.; Beale, G.; Schaffer, J.; Hsieh, B.-J.; Padalkar, S.; Rodriguezmoscoso, J.; Vinz, F.; Fernandez, K.

1987-01-01

A majority of the current expert systems focus on the symbolic-oriented logic and inference mechanisms of artificial intelligence (AI). Common rule-based systems employ empirical associations and are not well suited to deal with problems often arising in engineering. Described is a prototype expert system which combines both symbolic and numeric computing. The expert system's configuration is presented and its application to a spacecraft attitude control problem is discussed.

Measures of Light in Studies on Light-Driven Plant Plasticity in Artificial Environments

PubMed Central

Niinemets, Ülo; Keenan, Trevor F.

2012-01-01

Within-canopy variation in light results in profound canopy profiles in foliage structural, chemical, and physiological traits. Studies on within-canopy variations in key foliage traits are often conducted in artificial environments, including growth chambers with only artificial light, and greenhouses with and without supplemental light. Canopy patterns in these systems are considered to be representative to outdoor conditions, but in experiments with artificial and supplemental lighting, the intensity of artificial light strongly deceases with the distance from the light source, and natural light intensity in greenhouses is less than outdoors due to limited transmittance of enclosure walls. The implications of such changes in radiation conditions on canopy patterns of foliage traits have not yet been analyzed. We developed model-based methods for retrospective estimation of distance vs. light intensity relationships, for separation of the share of artificial and natural light in experiments with combined light and for estimation of average enclosure transmittance, and estimated daily integrated light at the time of sampling (Qint,C), at foliage formation (Qint,G), and during foliage lifetime (Qint,av). The implications of artificial light environments were analyzed for altogether 25 studies providing information on within-canopy gradients of key foliage traits for 70 species × treatment combinations. Across the studies with artificial light, Qint,G for leaves formed at different heights in the canopy varied from 1.8- to 6.4-fold due to changing the distance between light source and growing plants. In experiments with combined lighting, the share of natural light at the top of the plants varied threefold, and the share of natural light strongly increased with increasing depth in the canopy. Foliage nitrogen content was most strongly associated with Qint,G, but photosynthetic capacity with Qint,C, emphasizing the importance of explicit description of light environment during foliage lifetime. The reported and estimated transmittances of enclosures varied between 0.27 and 0.85, and lack of consideration of the reduction of light compared with outdoor conditions resulted in major underestimation of foliage plasticity to light. The study emphasizes that plant trait vs. light relationships in artificial systems are not directly comparable to natural environments unless modifications in lighting conditions in artificial environments are taken into account. PMID:22822407