WorldWideScience

Sample records for neural prosthetic applications

  1. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  2. Cortical neural prosthetics.

    Science.gov (United States)

    Schwartz, Andrew B

    2004-01-01

    Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity indefinitely. Information containing position and velocity correlates of animate movement needs to be extracted continuously in real time from the recorded activity. Prosthetic arms, the current effectors used in this work, need to have the agility and configuration of natural arms. Demonstrations using closed-loop control show that subjects change their neural activity to improve performance with these devices. Adaptive-learning algorithms that capitalize on these improvements show that this technology has the capability of restoring much of the arm movement lost with immobilizing deficits.

  3. Cognitive Control Signals for Neural Prosthetics

    National Research Council Canada - National Science Library

    S. Musallam; B. D. Corneil; B. Greger; H. Scherberger; R. A. Andersen

    2004-01-01

    Recent development of neural prosthetics for assisting paralyzed patients has focused on decoding intended hand trajectories from motor cortical neurons and using this signal to control external devices...

  4. Cochlear Implant Using Neural Prosthetics

    Science.gov (United States)

    Gupta, Shweta; Singh, Shashi kumar; Dubey, Pratik Kumar

    2012-10-01

    This research is based on neural prosthetic device. The oldest and most widely used of these electrical, and often computerized, devices is the cochlear implant, which has provided hearing to thousands of congenitally deaf people in this country. Recently, the use of the cochlear implant is expanding to the elderly, who frequently suffer major hearing loss. More cutting edge are artificial retinas, which are helping dozens of blind people see, and ìsmartî artificial arms and legs that amputees can maneuver by thoughts alone, and that feel more like real limbs.Research, which curiosity led to explore frog legs dancing during thunderstorms, a snail shapedorgan in the inner ear, and how various eye cells react to light, have fostered an understanding of how to ìtalkî to the nervous system. That understanding combined with the miniaturization of electronics and enhanced computer processing has enabled prosthetic devices that often can bridge the gap in nerve signaling that is caused by disease or injury.

  5. An Application Specific Instruction Set Processor (ASIP) for Adaptive Filters in Neural Prosthetics.

    Science.gov (United States)

    Xin, Yao; Li, Will X Y; Zhang, Zhaorui; Cheung, Ray C C; Song, Dong; Berger, Theodore W

    2015-01-01

    Neural coding is an essential process for neuroprosthetic design, in which adaptive filters have been widely utilized. In a practical application, it is needed to switch between different filters, which could be based on continuous observations or point process, when the neuron models, conditions, or system requirements have changed. As candidates of coding chip for neural prostheses, low-power general purpose processors are not computationally efficient especially for large scale neural population coding. Application specific integrated circuits (ASICs) do not have flexibility to switch between different adaptive filters while the cost for design and fabrication is formidable. In this research work, we explore an application specific instruction set processor (ASIP) for adaptive filters in neural decoding activity. The proposed architecture focuses on efficient computation for the most time-consuming matrix/vector operations among commonly used adaptive filters, being able to provide both flexibility and throughput. Evaluation and implementation results are provided to demonstrate that the proposed ASIP design is area-efficient while being competitive to commercial CPUs in computational performance.

  6. Real-time decision fusion for multimodal neural prosthetic devices.

    Science.gov (United States)

    White, James Robert; Levy, Todd; Bishop, William; Beaty, James D

    2010-03-02

    The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device. Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions. Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.

  7. Real-time decision fusion for multimodal neural prosthetic devices.

    Directory of Open Access Journals (Sweden)

    James Robert White

    Full Text Available BACKGROUND: The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device. METHODOLOGY/PRINCIPAL FINDINGS: Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs. Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions. CONCLUSIONS: Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.

  8. Neural-Network Control Of Prosthetic And Robotic Hands

    Science.gov (United States)

    Buckley, Theresa M.

    1991-01-01

    Electronic neural networks proposed for use in controlling robotic and prosthetic hands and exoskeletal or glovelike electromechanical devices aiding intact but nonfunctional hands. Specific to patient, who activates grasping motion by voice command, by mechanical switch, or by myoelectric impulse. Patient retains higher-level control, while lower-level control provided by neural network analogous to that of miniature brain. During training, patient teaches miniature brain to perform specialized, anthropomorphic movements unique to himself or herself.

  9. PERSPECTIVE: Consideration of user priorities when developing neural prosthetics

    Science.gov (United States)

    Anderson, Kim D.

    2009-10-01

    For too long there has been separation of basic science, biomedical engineering, clinical science and the people these disciplines are serving. A key ingredient to understanding the real-life consequences of many neurologic disorders that produce physical disabilities, such as spinal cord injury, is to obtain valuable information from the individuals that are actually living with the disorders everyday. This information can be obtained in an objective and usable format, which can then be used to direct biomedical research in a manner that is meaningful to the intended beneficiaries. In particular, the field of neural prosthetics for spinal cord injury can make great strides if user input is obtained throughout the stages of development. Presented here is the perspective of a scientist who also has 20 years of experience living with a cervical spinal cord injury.

  10. Modulation of grasping force in prosthetic hands using neural network-based predictive control.

    Science.gov (United States)

    Pasluosta, Cristian F; Chiu, Alan W L

    2015-01-01

    This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

  11. Nanoscale Properties of Neural Cell Prosthetic and Astrocyte Response

    Science.gov (United States)

    Flowers, D. A.; Ayres, V. M.; Delgado-Rivera, R.; Ahmed, I.; Meiners, S. A.

    2009-03-01

    Preliminary data from in-vivo investigations (rat model) suggest that a nanofiber prosthetic device of fibroblast growth factor-2 (FGF-2)-modified nanofibers can correctly guide regenerating axons across an injury gap with aligned functional recovery. Scanning Probe Recognition Microscopy (SPRM) with auto-tracking of individual nanofibers is used for investigation of the key nanoscale properties of the nanofiber prosthetic device for central nervous system tissue engineering and repair. The key properties under SPRM investigation include nanofiber stiffness and surface roughness, nanofiber curvature, nanofiber mesh density and porosity, and growth factor presentation and distribution. Each of these factors has been demonstrated to have global effects on cell morphology, function, proliferation, morphogenesis, migration, and differentiation. The effect of FGF-2 modification on the key nanoscale properties is investigated. Results from the nanofiber prosthetic properties investigations are correlated with astrocyte response to unmodified and FGF-2 modified scaffolds, using 2D planar substrates as a control.

  12. Real-time decision fusion for multimodal neural prosthetic devices

    National Research Council Canada - National Science Library

    White, James Robert; Levy, Todd; Bishop, William; Beaty, James D

    2010-01-01

    ...) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent...

  13. A dynamical systems view of motor preparation: Implications for neural prosthetic system design

    Science.gov (United States)

    Shenoy, Krishna V.; Kaufman, Matthew T.; Sahani, Maneesh; Churchland, Mark M.

    2013-01-01

    Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached. PMID:21763517

  14. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  15. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  16. Laser Science and its Applications in Prosthetic Rehabilitation

    OpenAIRE

    Gounder, Revathy; Gounder, Srinivasan

    2016-01-01

    The minimal invasive nature of lasers, with quick tissue response and healing has made them a very attractive technology in various fields of dentistry which serves as a tool to create a better result than ever before. The rapid development of lasers and their wavelengths with variety of applications on soft and hard tissues may continue to have major impact on the scope and practice in prosthetic dentistry. The purpose of this article is to make every clinician familiar with the fundamentals...

  17. Laser Science and its Applications in Prosthetic Rehabilitation

    Science.gov (United States)

    Gounder, Revathy; Gounder, Srinivasan

    2016-01-01

    The minimal invasive nature of lasers, with quick tissue response and healing has made them a very attractive technology in various fields of dentistry which serves as a tool to create a better result than ever before. The rapid development of lasers and their wavelengths with variety of applications on soft and hard tissues may continue to have major impact on the scope and practice in prosthetic dentistry. The purpose of this article is to make every clinician familiar with the fundamentals of lasers and different laser systems to incorporate into their clinical practices. PMID:28491254

  18. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex

    Science.gov (United States)

    Chestek, Cynthia A.; Gilja, Vikash; Nuyujukian, Paul; Foster, Justin D.; Fan, Joline M.; Kaufman, Matthew T.; Churchland, Mark M.; Rivera-Alvidrez, Zuley; Cunningham, John P.; Ryu, Stephen I.; Shenoy, Krishna V.

    2011-08-01

    Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

  19. Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

    Science.gov (United States)

    Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

    2014-12-01

    Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

  20. Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography

    Directory of Open Access Journals (Sweden)

    Manfredo Atzori

    2016-09-01

    Full Text Available Motivation: Natural control methods based on surface electromyography and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications and commercial prostheses are in the best case capable to offer natural control for only a few movements. Objective: In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its capabilities for the natural control of robotic hands via surface electromyography by providing a baseline on a large number of intact and amputated subjects. Methods: We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 hand amputated subjects. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets.Results: The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods but lower than the results obtained with the best reference methods in our tests. Significance: The results show that convolutional neural networks with a very simple architecture can produce accuracy comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters can be fundamental for the analysis of surface electromyography data. Finally, the results suggest that deeper and more complex networks may increase dexterous control robustness, thus contributing to bridge the gap between the market and scientific research

  1. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

    Science.gov (United States)

    Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

    2016-01-01

    Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

  2. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  3. Advances for prosthetic technology from historical perspective to current status to future application

    CERN Document Server

    LeMoyne, Robert

    2016-01-01

    This book focuses on the advances in transtibial prosthetic technology and targets research in the evolution of the powered prosthesis such as the BiOM, which was derived from considerable research and development at the Massachusetts Institute of Technology. The concept of the book spans the historical evolution of prosthetic applications from passive to new and futuristic robotic prosthetic technologies.  The author describes the reasons for amputation, surgical procedures, and an historical perspective of the prosthesis for the lower limb. He also addresses the phases and sub-phases of gait and compensatory mechanisms arising for a transtibial prosthesis and links the compensatory mechanisms to long-term morbidities.  The general technologies for gait analysis central to prosthetic design and the inherent biomechanics foundations for analysis are also explored.  The book reports on recent-past to current-term applications with passive elastic prostheses.  The core of the book deals with futuristic robo...

  4. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  5. Laser photonics application in prosthetic dentistry for denture design optimization

    Science.gov (United States)

    Grosmann, M. H.; Kiryushin, M. A.; Larkin, A. I.; Lebedenko, A. I.; Lebedenko, I. Yu.; Lopatina, N. A.; Osincev, A. V.; Shchepinov, V. P.; Shchepinova, I. V.

    2010-06-01

    The aim of this work is to demonstrate that holographic and speckle interferometry—laser photonics methods are compatible and useful for prosthetic stomatology. These methods allow to study the deformation of the mandible after insertion of mini-implants of various forms, and to give the practical medical recommendations.

  6. A virtual reality environment for designing and fitting neural prosthetic limbs.

    Science.gov (United States)

    Hauschild, Markus; Davoodi, Rahman; Loeb, Gerald E

    2007-03-01

    Building and testing novel prosthetic limbs and control algorithms for functional electrical stimulation (FES) is expensive and risky. Here, we describe a virtual reality environment (VRE) to facilitate and accelerate the development of novel systems. In the VRE, subjects/patients can operate a simulated limb to interact with virtual objects. Realistic models of all relevant musculoskeletal and mechatronic components allow the development of entire prosthetic systems in VR before introducing them to the patient. The system is used both by engineers as a development tool and by clinicians to fit prosthetic devices to patients.

  7. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...

  9. New optimum humanoid hand design for prosthetic applications.

    Science.gov (United States)

    El-Sheikh, Mogeeb A; Taher, Mona F; Metwalli, Sayed M

    2012-04-30

    To address the need for a commercially feasible prosthetic hand, the current work presents the design of a new humanoid hand actuated using shape memory alloy (SMA) artificial muscle wires. The hand has 3 compliant fingers and a thumb attached to the palm. The palm structure is a novel design, which is based on the natural arches of the human hand to provide better grasping capabilities. A compact actuator module is proposed to house and cool the SMA wires. Design parameters of the hand were selected to maximize the work density. The hand is lightweight, low cost, and operates silently. It has functional opening and closing speeds and fingertip force.

  10. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  11. Occlusal concepts application in resolving implant prosthetic failure: case report.

    Science.gov (United States)

    Jamcoski, Vanessa Helena; Faot, Fernanda; de Mattias Sartori, Ivete Aparecida; Vieira, Rogéria Acedo; Tiossi, Rodrigo

    2014-04-01

    The prosthetic management of a poor implant treatment is presented in this case report. The recommended occlusion concepts for implant-supported prostheses were applied for the resolution of the case. The rehabilitation of the posterior segments provided a mutually protected occlusion with adequate distribution of the axial and lateral bite forces with stable posterior occlusion. The clinical exam indicated the need for modification in the vertical dimension of occlusion. Sufficient interocclusal rest space was present to test the alteration in the vertical dimension. The aim was to achieve an occlusion scheme that followed four specific criteria: (1) centric contacts and centric relation of the jaw-to-jaw position; (2) anterior guidance only; (3) shallow anterior angle of tooth contact; and (4) vertical dimension of occlusion with acceptable tooth form and guidance. The success of an oral rehabilitation relies in following the aforementioned criteria, appropriate interaction between the dental laboratory technician and the clinician, careful elaboration of the provisional rehabilitation with all the desired details to be reproduced in the final prosthetic restoration and sufficient follow-up time of the provisional prostheses before placing the final restoration.

  12. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  13. Neural network model of vestibular nuclei reaction to onset of vestibular prosthetic stimulation

    Directory of Open Access Journals (Sweden)

    Jack eDigiovanna

    2016-04-01

    Full Text Available The vestibular system incorporates multiple sensory pathways to provide crucial information about head and body motion. Damage to the semicircular canals, the peripheral vestibular organs that sense rotational velocities of the head, can severely degrade the ability to perform activities of daily life. Vestibular prosthetics address this problem by using stimulating electrodes that can trigger primary vestibular afferents to modulate their firing rates, thus encoding head movement. These prostheses have been demonstrated chronically in multiple animal models and acutely tested in short-duration trials within the clinic in humans. However, mainly due to limited opportunities to fully characterize stimulation parameters, there is a lack of understanding of ‘optimal’ stimulation configurations for humans. Here we model possible adaptive plasticity in the vestibular pathway. Specifically, this model highlights the influence of adaptation of synaptic strengths and offsets in the vestibular nuclei to compensate for the initial activation of the prosthetic. By changing the synaptic strengths, the model is able to replicate the clinical observation that erroneous eye movements are attenuated within 30 minutes without any change to the prosthetic stimulation rate. Although our model was only built to match this time-point, we further examined how it affected subsequent pulse rate and pulse amplitude modulation. Pulse amplitude modulation was more effective than pulse rate modulation for nearly all stimulation configurations during these acute tests. Two non-intuitive relationships highlighted by our model explain this performance discrepancy. Specifically the attenuation of synaptic strengths for afferents stimulated during baseline adaptation and the discontinuity between baseline and residual firing rates both disproportionally boost pulse amplitude modulation. Co-modulation of pulse rate and amplitude has been experimentally shown to induce both

  14. A VLSI field-programmable mixed-signal array to perform neural signal processing and neural modeling in a prosthetic system.

    Science.gov (United States)

    Bamford, Simeon A; Hogri, Roni; Giovannucci, Andrea; Taub, Aryeh H; Herreros, Ivan; Verschure, Paul F M J; Mintz, Matti; Del Giudice, Paolo

    2012-07-01

    A very-large-scale integration field-programmable mixed-signal array specialized for neural signal processing and neural modeling has been designed. This has been fabricated as a core on a chip prototype intended for use in an implantable closed-loop prosthetic system aimed at rehabilitation of the learning of a discrete motor response. The chosen experimental context is cerebellar classical conditioning of the eye-blink response. The programmable system is based on the intimate mixing of switched capacitor analog techniques with low speed digital computation; power saving innovations within this framework are presented. The utility of the system is demonstrated by the implementation of a motor classical conditioning model applied to eye-blink conditioning in real time with associated neural signal processing. Paired conditioned and unconditioned stimuli were repeatedly presented to an anesthetized rat and recordings were taken simultaneously from two precerebellar nuclei. These paired stimuli were detected in real time from this multichannel data. This resulted in the acquisition of a trigger for a well-timed conditioned eye-blink response, and repetition of unpaired trials constructed from the same data led to the extinction of the conditioned response trigger, compatible with natural cerebellar learning in awake animals.

  15. Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

    Science.gov (United States)

    Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

    2016-08-31

    Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

  16. A New Animal Model for Developing a Somatosensory Neural Interface for Prosthetic Limbs

    Science.gov (United States)

    2008-02-12

    interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D. University of Pittsburgh 1 10/15/2007 Scientific progress and accomplishments. We...information to the brain. A new animal model for developing a somatosensory neural interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D...A new animal model for developing a somatosensory neural interface for neuroprosthetic limbs. PI: Douglas J. Weber, Ph.D. University of Pittsburgh

  17. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  18. Design for a three-fingered hand. [robotic and prosthetic applications

    Science.gov (United States)

    Crossley, F. R. E.

    1977-01-01

    This paper describes the construction of a prototype mechanical hand or 'end effector' for use on a remotely controlled robot, but with possible application as a prosthetic device. An analysis of hand motions is reported, from which it is concluded that the two most important manipulations (apart from grasps) are to be able to pick up a tool and draw it into a nested grip against the palm, and to be able to hold a pistol-grip tool such as an electric drill and pull the trigger. One of our models was tested and found capable of both these operations.

  19. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    methods. That is why it is becoming popular in various fields including coastal engineering. Waves and tides will play important roles in coastal erosion or accretion. This paper briefly describes the back-propagation neural networks and its application...

  20. Fabrication, sensation and control of fluidic elastomer actuators and their application towards hand orthotics and prosthetics

    Science.gov (United States)

    Zhao, Huichan

    Due to their continuous and natural motion, fluidic elastomer actuators (FEAs) have shown potential in a range of robotic applications including prosthetics and orthotics. Despite their advantages and rapid developments, robots using these actuators still have several challenging issues to be addressed. First, the reliable production of low cost and complex actuators that can apply high forces is necessary, yet none of existing fabrication methods are both easy to implement and of high force output. Next, compliant or stretchable sensors that can be embedded into their bodies for sophisticated functions are required, however, many of these sensors suffer from hysteresis, fabrication complexity, chemical safety and environmental instability, and material incompatibility with soft actuators. Finally, feedback control for FEAs is necessary to achieve better performance, but most soft robots are still "open-loop". In this dissertation, I intend to help solve the above issues and drive the applications of soft robotics towards hand orthotics and prosthetics. First, I adapt rotational casting as a new manufacturing method for soft actuators. I present a cuboid soft actuator that can generate a force of >25 N at its tip, a near ten-fold increase over similar actuators previously reported. Next, I propose a soft orthotic finger with position control enabled via embedded optical fiber. I monitor both the static and dynamic states via the optical sensor and achieve the prescribed curvatures accurately and with stability by a gain-scheduled proportional-integral-derivative controller. Then I develop the soft orthotic fingers into a low-cost, closed-loop controlled, soft orthotic glove that can be worn by a typical human hand and helpful for grasping light objects, while also providing finger position control. I achieve motion control with inexpensive, binary pneumatic switches controlled by a simple finite-state-machine. Finally, I report the first use of stretchable optical

  1. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  2. Applications of Pulse-Coupled Neural Networks

    CERN Document Server

    Ma, Yide; Wang, Zhaobin

    2011-01-01

    "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Sci

  3. Neural networks advances and applications 2

    CERN Document Server

    Gelenbe, E

    1992-01-01

    The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

  4. An investigation on effects of amputee's physiological parameters on maximum pressure developed at the prosthetic socket interface using artificial neural network.

    Science.gov (United States)

    Nayak, Chitresh; Singh, Amit; Chaudhary, Himanshu; Unune, Deepak Rajendra

    2017-10-23

    Technological advances in prosthetics have attracted the curiosity of researchers in monitoring design and developments of the sockets to sustain maximum pressure without any soft tissue damage, skin breakdown, and painful sores. Numerous studies have been reported in the area of pressure measurement at the limb/socket interface, though, the relation between amputee's physiological parameters and the pressure developed at the limb/socket interface is still not studied. Therefore, the purpose of this work is to investigate the effects of patient-specific physiological parameters viz. height, weight, and stump length on the pressure development at the transtibial prosthetic limb/socket interface. Initially, the pressure values at the limb/socket interface were clinically measured during stance and walking conditions for different patients using strain gauges placed at critical locations of the stump. The measured maximum pressure data related to patient's physiological parameters was used to develop an artificial neural network (ANN) model. The effects of physiological parameters on the pressure development at the limb/socket interface were examined using the ANN model. The analyzed results indicated that the weight and stump length significantly affects the maximum pressure values. The outcomes of this work could be an important platform for the design and development of patient-specific prosthetic socket which can endure the maximum pressure conditions at stance and ambulation conditions.

  5. [Application and outlook of three-dimensional printing in prosthetic dentistry].

    Science.gov (United States)

    Sun, Y C; Li, R; Zhou, Y S; Wang, Y

    2017-06-09

    At present, three-dimensional (3D) printing has been applied in many aspects in the field of prosthodontics, such as dental models, wax patterns, guide plates, dental restoration and customized implants. The common forming principles include light curing, sintering and melting-condensation, the materials include pure wax, resin, metal and ceramics. However, the printing precision and the strength of multi-material integrated forming, remains to be improved. In addition, as a technology by which the internal structure of a material can be customized manufacturing, further advantage of 3D printing used in the manufacture of dental restoration lies in the customization functional bionic micro-structures, but the related research is still in its infancy. The review briefly summarizes the commonly used 3D printing crafts in prosthetic dentistry, and details clinical applications and evaluations, provides references for clinical decision and further research.

  6. Rehabilitation and Prosthetic Services

    Science.gov (United States)

    ... Sensory Aids Service » Prosthetic & Sensory Aids Service (PSAS) Rehabilitation and Prosthetic Services Menu Menu Rehabilitation and Prosthetics Rehabilitation and Prosthetic Services Home Amputation ...

  7. Bonding strength of glass-ceramic trabecular-like coatings to ceramic substrates for prosthetic applications.

    Science.gov (United States)

    Chen, Qiang; Baino, Francesco; Pugno, Nicola M; Vitale-Brovarone, Chiara

    2013-04-01

    A new approach based on the concepts of quantized fracture mechanics (QFM) is presented and discussed in this paper to estimate the bonding strength of trabecular-like coatings, i.e. glass-ceramic scaffolds mimicking the architecture of cancellous bone, to ceramic substrates. The innovative application of glass-derived scaffolds as trabecular-like coatings is proposed in order to enhance the osteointegration of prosthetic ceramic devices. The scaffolds, prepared by polymeric sponge replication, are joined to alumina substrates by a dense glass-ceramic coating (interlayer) and the so-obtained 3-layer constructs are investigated from micro-structural, morphological and mechanical viewpoints. In particular, the fracture strengths of three different crack propagation modes, i.e. glass-derived scaffold fracture, interface delamination or mixed fracture, are predicted in agreement with those of experimental mechanical tests. The approach proposed in this work could have interesting applications towards an ever more rational design of bone tissue engineering biomaterials and coatings, in view of the optimization of their mechanical properties for making them actually suitable for clinical applications. Copyright © 2012 Elsevier B.V. All rights reserved.

  8. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    user

    An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

  9. Improved Extension Neural Network and Its Applications

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2014-01-01

    Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

  10. Artificial neural network applications in ionospheric studies

    Directory of Open Access Journals (Sweden)

    L. R. Cander

    1998-06-01

    Full Text Available The ionosphere of Earth exhibits considerable spatial changes and has large temporal variability of various timescales related to the mechanisms of creation, decay and transport of space ionospheric plasma. Many techniques for modelling electron density profiles through entire ionosphere have been developed in order to solve the "age-old problem" of ionospheric physics which has not yet been fully solved. A new way to address this problem is by applying artificial intelligence methodologies to current large amounts of solar-terrestrial and ionospheric data. It is the aim of this paper to show by the most recent examples that modern development of numerical models for ionospheric monthly median long-term prediction and daily hourly short-term forecasting may proceed successfully applying the artificial neural networks. The performance of these techniques is illustrated with different artificial neural networks developed to model and predict the temporal and spatial variations of ionospheric critical frequency, f0F2 and Total Electron Content (TEC. Comparisons between results obtained by the proposed approaches and measured f0F2 and TEC data provide prospects for future applications of the artificial neural networks in ionospheric studies.

  11. Neural network models: Insights and prescriptions from practical applications

    Energy Technology Data Exchange (ETDEWEB)

    Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)

    1995-12-31

    Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.

  12. Application of Orthodromic Island Flap Prosthetics of Homo-Digital Artery in Finger-Tip Defect

    Directory of Open Access Journals (Sweden)

    Daming Lu

    2014-03-01

    Full Text Available Objective: To investigate the clinical efficacy of orthodromic island flap prosthetics of homodigital artery on finger-tip defect. Methods: A total of 21 patients with finger-tip defect from December, 2010 to April, 2013 were given orthodromic island flap prosthetics of homo-digital artery, with the maximum and minimum sizes of flaps being 20 mm×22 mm and 10 mm×15 mm, respectively. Results: All patients with finger-tip defect survived from the flap surgery and the wounds were favorably healed. 3 - 12 months follow-up after operation, the flaps were observed with approving appearance, soft texture and favorable elasticity, with two-point discrimination being 6 - 8 mm. According to TAM detection of hand functions, flaps were excellent healed in 19 cases, good and fairish in 1 case respectively, with effective rate being 95.2%. Conclusion: Orthodromic island flap prosthetics of homo-digital artery is simple and safe in operation with satisfactory effcacy, being the most ideal method for the repair of finger-tip defect.

  13. Development and Evaluation of Micro-Electrocorticography Arrays for Neural Interfacing Applications

    Science.gov (United States)

    Schendel, Amelia Ann

    Neural interfaces have great promise for both electrophysiological research and therapeutic applications. Whether for the study of neural circuitry or for neural prosthetic or other therapeutic applications, micro-electrocorticography (micro-ECoG) arrays have proven extremely useful as neural interfacing devices. These devices strike a balance between invasiveness and signal resolution, an important step towards eventual human application. The objective of this research was to make design improvements to micro-ECoG devices to enhance both biocompatibility and device functionality. To best evaluate the effectiveness of these improvements, a cranial window imaging method for in vivo monitoring of the longitudinal tissue response post device implant was developed. Employment of this method provided valuable insight into the way tissue grows around micro-ECoG arrays after epidural implantation, spurring a study of the effects of substrate geometry on the meningeal tissue response. The results of the substrate footprint comparison suggest that a more open substrate geometry provides an easy path for the tissue to grow around to the top side of the device, whereas a solid device substrate encourages the tissue to thicken beneath the device, between the electrode sites and the brain. The formation of thick scar tissue between the recording electrode sites and the neural tissue is disadvantageous for long-term recorded signal quality, and thus future micro-ECoG device designs should incorporate open-architecture substrates for enhanced longitudinal in vivo function. In addition to investigating improvements for long-term device reliability, it was also desired to enhance the functionality of micro-ECoG devices for neural electrophysiology research applications. To achieve this goal, a completely transparent graphene-based device was fabricated for use with the cranial window imaging method and optogenetic techniques. The use of graphene as the conductive material provided

  14. Fuzzy logic and neural networks basic concepts & application

    CERN Document Server

    Alavala, Chennakesava R

    2008-01-01

    About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank

  15. Fuzzy neural networks: theory and applications

    Science.gov (United States)

    Gupta, Madan M.

    1994-10-01

    During recent years, significant advances have been made in two distinct technological areas: fuzzy logic and computational neural networks. The theory of fuzzy logic provides a mathematical framework to capture the uncertainties associated with human cognitive processes, such as thinking and reasoning. It also provides a mathematical morphology to emulate certain perceptual and linguistic attributes associated with human cognition. On the other hand, the computational neural network paradigms have evolved in the process of understanding the incredible learning and adaptive features of neuronal mechanisms inherent in certain biological species. Computational neural networks replicate, on a small scale, some of the computational operations observed in biological learning and adaptation. The integration of these two fields, fuzzy logic and neural networks, have given birth to an emerging technological field -- fuzzy neural networks. Fuzzy neural networks, have the potential to capture the benefits of these two fascinating fields, fuzzy logic and neural networks, into a single framework. The intent of this tutorial paper is to describe the basic notions of biological and computational neuronal morphologies, and to describe the principles and architectures of fuzzy neural networks. Towards this goal, we develop a fuzzy neural architecture based upon the notion of T-norm and T-conorm connectives. An error-based learning scheme is described for this neural structure.

  16. Prosthetic EMG control enhancement through the application of man-machine principles

    Science.gov (United States)

    Simcox, W. A.

    1977-01-01

    An area in medicine that appears suitable to man-machine principles is rehabilitation research, particularly when the motor aspects of the body are involved. If one considers the limb, whether functional or not, as the machine, the brain as the controller and the neuromuscular system as the man-machine interface, the human body is reduced to a man-machine system that can benefit from the principles behind such systems. The area of rehabilitation that this paper deals with is that of an arm amputee and his prosthetic device. Reducing this area to its man-machine basics, the problem becomes one of attaining natural multiaxis prosthetic control using Electromyographic activity (EMG) as the means of communication between man and prothesis. In order to use EMG as the communication channel it must be amplified and processed to yield a high information signal suitable for control. The most common processing scheme employed is termed Mean Value Processing. This technique for extracting the useful EMG signal consists of a differential to single ended conversion to the surface activity followed by a rectification and smoothing.

  17. Robotic hand with locking mechanism using TCP muscles for applications in prosthetic hand and humanoids

    Science.gov (United States)

    Saharan, Lokesh; Tadesse, Yonas

    2016-04-01

    This paper presents a biomimetic, lightweight, 3D printed and customizable robotic hand with locking mechanism consisting of Twisted and Coiled Polymer (TCP) muscles based on nylon precursor fibers as artificial muscles. Previously, we have presented a small-sized biomimetic hand using nylon based artificial muscles and fishing line muscles as actuators. The current study focuses on an adult-sized prosthetic hand with improved design and a position/force locking system. Energy efficiency is always a matter of concern to make compact, lightweight, durable and cost effective devices. In natural human hand, if we keep holding objects for long time, we get tired because of continuous use of energy for keeping the fingers in certain positions. Similarly, in prosthetic hands we also need to provide energy continuously to artificial muscles to hold the object for a certain period of time, which is certainly not energy efficient. In this work we, describe the design of the robotic hand and locking mechanism along with the experimental results on the performance of the locking mechanism.

  18. Application of Neural Networks to House Pricing and Bond Rating

    NARCIS (Netherlands)

    Daniëls, H.A.M.; Kamp, B.; Verkooijen, W.J.H.

    1997-01-01

    Feed forward neural networks receive a growing attention as a data modelling tool in economic classification problems. It is well-known that controlling the design of a neural network can be cumbersome. Inaccuracies may lead to a manifold of problems in the application such as higher errors due to

  19. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  20. Application of Neural Networks for Energy Reconstruction

    CERN Document Server

    Damgov, Jordan

    2002-01-01

    The possibility to use Neural Networks for reconstruction ofthe energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed-forward neural network, good linearity, Gaussian energy distribution and good energy resolution can be achieved. Significant improvement of the energy resolution and linearity is reached in comparison with other weighting methods for energy reconstruction.

  1. The Reproducibility and Applicability of an EFD(®) Dispenser in the Prosthetic Technology of Maxillofacial Prostheses.

    Science.gov (United States)

    Blokland, L; Borsboom, P C F; Stokman, M A; Reintsema, H; van Oort, R P

    2013-09-01

    A reproducible method of dosing pigments can be beneficial and more efficient in the current colour matching procedure in maxillofacial prosthetics. In this study the reproducibility and applicability for pigment dosing of a commercial available EFD(®) dispenser were tested. The reproducibility of a Performus™ II type EFD(®) dispenser was tested by repeating dosing experiments with a set of eight syringes filled with pigment pastes (Factor 2; Flagstaff, USA). To evaluate conventional colour matching, four conventionally colour matched samples were polymerized and compared to the original ones. To investigate the reproducibility of the dispenser in practice, a fifth recipe was dispensed 10 times and colour differences were evaluated visually and as well calculated from measurements with a colour and translucency meter (CTM, PBSensortechnology bv). All dispensed amounts of pigment pastes showed a coefficient of variation in weight of less than 10 %. Evaluating the reproductions of four skin batches compared to the original batches, a ∆E2000 colour difference of 3-7 was measured. Evaluating ten reproductions of one skin coloured batch made with the dispenser, color difference ∆E2000 values compared to the average L*a*b* values, were less than 2 and no visual colour differences could be estimated. Conform these results, low colour differences could be measured with the CTM, indicating no visually observable consequences. Despite the estimated coefficient of variation, the reproducibility of the EFD(®) dispenser in terms of colour difference ∆E2000 of successive dispensing is applicable for colour reproduction in facial prosthetics. Segregation of the current color pastes in due time needs to be taken into consideration.

  2. Application of artificial neural networks (ANNs) in wine technology.

    Science.gov (United States)

    Baykal, Halil; Yildirim, Hatice Kalkan

    2013-01-01

    In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.

  3. Novel differential mechanism enabling two DOF from a single actuator: application to a prosthetic hand.

    Science.gov (United States)

    Belter, Joseph T; Dollar, Aaron M

    2013-06-01

    There will always be a drive to reduce the complexity, weight, and cost of mobile platforms while increasing their inherent capabilities. This paper presents a novel method of increasing the range of achievable grasp configurations of a mechatronic hand controlled by a single actuator. By utilizing the entire actuator space, the hand is able to perform four grasp types (lateral, precision, precision/power, and power) with a single input resulting in a potentially lighter and simpler hand design. We demonstrate this strategy in a prototype hand that is evaluated to determine the benefit of this method over the addition of a second actuator. Results show a decrease in weight but a 0.8 sec transition time between grasp types with the proposed method. The prototype hand can be controlled by a single EMG signal that can command a change in grasp type or an opening/closing of the hand. We discuss the potential of this mechanism to improve prosthetic hand design as compared to current myoelectric systems.

  4. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  5. Complex-valued neural networks advances and applications

    CERN Document Server

    Hirose, Akira

    2013-01-01

    Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

  6. Hybrid digital signal processing and neural networks applications in PWRs

    Energy Technology Data Exchange (ETDEWEB)

    Eryurek, E.; Upadhyaya, B.R.; Kavaklioglu, K.

    1991-12-31

    Signal validation and plant subsystem tracking in power and process industries require the prediction of one or more state variables. Both heteroassociative and auotassociative neural networks were applied for characterizing relationships among sets of signals. A multi-layer neural network paradigm was applied for sensor and process monitoring in a Pressurized Water Reactor (PWR). This nonlinear interpolation technique was found to be very effective for these applications.

  7. Type-2 fuzzy neural networks and their applications

    CERN Document Server

    Aliev, Rafik Aziz

    2014-01-01

    This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

  8. Neural networks: Application to medical imaging

    Science.gov (United States)

    Clarke, Laurence P.

    1994-01-01

    The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

  9. Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications

    Directory of Open Access Journals (Sweden)

    Giuseppina Gini

    2012-01-01

    Full Text Available One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN and wavelet features.

  10. Application of the minimum fuel neural network to music signals

    DEFF Research Database (Denmark)

    Harbo, Anders La-Cour

    2004-01-01

    Finding an optimal representation of a signal in an over-complete dictionary is often quite difficult. Since general results in this field are not very application friendly it truly helps to specify the framework as much as possible. We investigate the method Minimum Fuel Neural Network (MFNN...

  11. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  12. Physico-chemical characterization of functionalized polypropylenic fibers for prosthetic applications

    Science.gov (United States)

    Nisticò, Roberto; Faga, Maria Giulia; Gautier, Giovanna; Magnacca, Giuliana; D'Angelo, Domenico; Ciancio, Emanuele; Piacenza, Giacomo; Lamberti, Roberta; Martorana, Selanna

    2012-08-01

    Polypropylene (PP) fibers can be manufactured to form nets which can find application as prosthesis in hernioplasty. One of the most important problem to deal with when nets are applied in vivo consists in the reproduction of bacteria within the net fibers intersections. This occurs right after the application of the prosthesis, and causes infections, thus it is fundamental to remove bacteria in the very early stage of the nets application. This paper deals with the physico-chemical characterization of such nets, pre-treated by atmospheric pressure plasma dielectric barrier discharge apparatus (APP-DBD) and functionalized with an antibiotic drug such as chitosan. The physico-chemical characterization of sterilized nets, before and after the functionalization with chitosan, was carried out by means of scanning electron microscopy (SEM) coupled with EDS spectroscopy, FTIR spectroscopy, drop shape analysis (DSA), X-ray diffraction and thermal analyses (TGA and DSC). The aim of the work is to individuate a good strategy to characterize this kind of materials, to understand the effects of polypropylene pre-treatment on functionalization efficiency, to follow the materials ageing in order to study the effects of the surface treatment for in vivo applications.

  13. Non-linear feedback neural networks VLSI implementations and applications

    CERN Document Server

    Ansari, Mohd Samar

    2014-01-01

    This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

  14. Real-time applications of neural nets

    Energy Technology Data Exchange (ETDEWEB)

    Spencer, J.E.

    1989-05-01

    Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.

  15. Neural networks for process control and optimization: two industrial applications.

    Science.gov (United States)

    Bloch, Gérard; Denoeux, Thierry

    2003-01-01

    The two most widely used neural models, multilayer perceptron (MLP) and radial basis function network (RBFN), are presented in the framework of system identification and control. The main steps for building such nonlinear black box models are regressor choice, selection of internal architecture, and parameter estimation. The advantages of neural network models are summarized: universal approximation capabilities, flexibility, and parsimony. Two applications are described in steel industry and water treatment, respectively, the control of alloying process in a hot dipped galvanizing line and the control of a coagulation process in a drinking water treatment plant. These examples highlight the interest of neural techniques, when complex nonlinear phenomena are involved, but the empirical knowledge of control operators can be learned.

  16. SU-8-based microneedles for in vitro neural applications

    Science.gov (United States)

    Altuna, Ane; Gabriel, Gemma; Menéndez de la Prida, Liset; Tijero, María; Guimerá, Anton; Berganzo, Javier; Salido, Rafa; Villa, Rosa; Fernández, Luis J.

    2010-06-01

    This paper presents novel design, fabrication, packaging and the first in vitro neural activity recordings of SU-8-based microneedles. The polymer SU-8 was chosen because it provides excellent features for the fabrication of flexible and thin probes. A microprobe was designed in order to allow a clean insertion and to minimize the damage caused to neural tissue during in vitro applications. In addition, a tetrode is patterned at the tip of the needle to obtain fine-scale measurements of small neuronal populations within a radius of 100 µm. Impedance characterization of the electrodes has been carried out to demonstrate their viability for neural recording. Finally, probes are inserted into 400 µm thick hippocampal slices, and simultaneous action potentials with peak-to-peak amplitudes of 200-250 µV are detected.

  17. An artificial neural network controller for intelligent transportation systems applications

    Energy Technology Data Exchange (ETDEWEB)

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J. [Argonne National Lab., IL (United States). Reactor Analysis Div.

    1996-04-01

    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 applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  18. Small-signal neural models and their applications.

    Science.gov (United States)

    Basu, Arindam

    2012-02-01

    This paper introduces the use of the concept of small-signal analysis, commonly used in circuit design, for understanding neural models. We show that neural models, varying in complexity from Hodgkin-Huxley to integrate and fire have similar small-signal models when their corresponding differential equations are close to the same bifurcation with respect to input current. Three applications of small-signal neural models are shown. First, some of the properties of cortical neurons described by Izhikevich are explained intuitively through small-signal analysis. Second, we use small-signal models for deriving parameters for a simple neural model (such as resonate and fire) from a more complicated but biophysically relevant one like Morris-Lecar. We show similarity in the subthreshold behavior of the simple and complicated model when they are close to a Hopf bifurcation and a saddle-node bifurcation. Hence, this is useful to correctly tune simple neural models for large-scale cortical simulations. Finaly, the biasing regime of a silicon ion channel is derived by comparing its small-signal model with a Hodgkin-Huxley-type model.

  19. AI and Prosthetics

    OpenAIRE

    Kyriazi, Nefeli Evdokia

    2016-01-01

    Prosthetics are very important to an amputee.The introduction of technology to prosthetics has allowed bionic limbs to emerge and change the way we were thinking about prosthetic limbs.More and more companies create new innovative models,but not affordable for anyone.3D Printing gives more options.

  20. Workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hashem, S.

    1995-03-01

    This report consists of the abstracts for the papers given at the conference. Applications of neural networks in the environmental, energy and biomedical fields are discussed. Some of the topics covered are: predicting atmospheric pollutant concentrations due to fossil-fired electric power generation; hazardous waste characterization; nondestructive TRU (transuranic) waste assay; risk analysis; load forecasting for electric utilities; design of a wind power storage and generation system; nuclear fuel management; etc.

  1. Neuronmaster: an integrated tool for applications in neural networks

    Science.gov (United States)

    Rivas-Echeverria, Francklin; Colina-Morles, Eliezer; Sole, Solazver; Perez-Mendez, Anna; Bravo-Bravo, Cesar; Bravo-Bravo, Victor

    2001-03-01

    This work presents the design of an integral environment for the suitable development of neural networks applications. The integrated environment contemplates the following features: A data processing module which encompasses statistical data analysis techniques for variables selection reduction, a variety of learning algorithms, code generator for different computer languages to enable network implementation, a learning sessions planning module and database connectivity facilities via ODBC, RPC, and API.

  2. Artificial Neural Networks Application in Modal Analysis of Tires

    Science.gov (United States)

    Koštial, P.; Jančíková, Z.; Bakošová, D.; Valíček, J.; Harničárová, M.; Špička, I.

    2013-10-01

    The paper deals with the application of artificial neural networks (ANN) to tires' own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

  3. Review On Applications Of Neural Network To Computer Vision

    Science.gov (United States)

    Li, Wei; Nasrabadi, Nasser M.

    1989-03-01

    Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

  4. Application of neural networks to waste site screening

    Energy Technology Data Exchange (ETDEWEB)

    Dabiri, A.E.; Garrett, M.; Kraft, T.; Hilton, J.; VanHammersveld, M.

    1993-02-01

    Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach consists primarily of drilling boreholes near contaminated sites and chemically analyzing the extracted physical samples and processing the data. This is expensive and time consuming. The feasibility of using neural network techniques to reduce the cost of waste site screening was investigated. Two neural network techniques, gradient descent back propagation and fully recurrent back propagation were utilized. The networks were trained with data received from Westinghouse Hanford Corporation. The results indicate that the network trained with the fully recurrent technique shows satisfactory generalization capability. The predicted results are close to the results obtained from a mathematical flow prediction model. It is possible to develop a new tool to predict the waste plume, thus substantially reducing the number of the bore sites and samplings. There are a variety of applications for this technique in environmental site screening and remediation. One of the obvious applications would be for optimum well siting. A neural network trained from the existing sampling data could be utilized to decide where would be the best position for the next bore site. Other applications are discussed in the report.

  5. Threshold concepts in prosthetics

    OpenAIRE

    Hill, Sophie

    2016-01-01

    Background: Curriculum documents identify key concepts within learning prosthetics. Threshold concepts provide an alternative way of viewing the curriculum, focussing on the ways of thinking and practicing within prosthetics. Threshold concepts can be described as an opening to a different way of viewing a concept. This article forms part of a larger study exploring what students and staff experience as difficult in learning about prosthetics. Objectives: To explore possible thresh...

  6. Prevention of Prosthetic Dentistry

    Directory of Open Access Journals (Sweden)

    Eremin O.V.

    2011-03-01

    Full Text Available Prevention in prosthetic dentistry is not just a regular oral hygiene and the prevention of caries in the early stages of its development. The initial goal of orthopedic and dental should be the ability to convey to the patient's sense of pros-thetics that proteziruya one saved more. An example is included prosthetic dental arch defects with bridges or single artificial crowns on implants that will prevent movement of teeth and the continuity of the dentition

  7. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  8. Threshold concepts in prosthetics.

    Science.gov (United States)

    Hill, Sophie

    2017-12-01

    Curriculum documents identify key concepts within learning prosthetics. Threshold concepts provide an alternative way of viewing the curriculum, focussing on the ways of thinking and practicing within prosthetics. Threshold concepts can be described as an opening to a different way of viewing a concept. This article forms part of a larger study exploring what students and staff experience as difficult in learning about prosthetics. To explore possible threshold concepts within prosthetics. Qualitative, interpretative phenomenological analysis. Data from 18 students and 8 staff at two universities with undergraduate prosthetics and orthotics programmes were generated through interviews and questionnaires. The data were analysed using an interpretative phenomenological analysis approach. Three possible threshold concepts arose from the data: 'how we walk', 'learning to talk' and 'considering the person'. Three potential threshold concepts in prosthetics are suggested with possible implications for prosthetics education. These possible threshold concepts involve changes in both conceptual and ontological knowledge, integrating into the persona of the individual. This integration occurs through the development of memories associated with procedural concepts that combine with disciplinary concepts. Considering the prosthetics curriculum through the lens of threshold concepts enables a focus on how students learn to become prosthetists. Clinical relevance This study provides new insights into how prosthetists learn. This has implications for curriculum design in prosthetics education.

  9. Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-Inspired Robotic Hand for Prosthetic Applications.

    Science.gov (United States)

    Fani, Simone; Bianchi, Matteo; Jain, Sonal; Pimenta Neto, José Simões; Boege, Scott; Grioli, Giorgio; Bicchi, Antonio; Santello, Marco

    2016-01-01

    Myoelectric artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed so as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an underactuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e., flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography-to-position mapping ensured the

  10. Artificial neural nets application in the cotton yarn industry

    Directory of Open Access Journals (Sweden)

    Gilberto Clóvis Antoneli

    2016-06-01

    Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

  11. Neural computation and particle accelerators research, technology and applications

    CERN Document Server

    D'Arras, Horace

    2010-01-01

    This book discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons. Accelerators have a very broad range of applications in many industrial fields, from high energy physics to medical isotope production. Nuclear technology is one of the fields discussed in this book. The development that has been reached by particle accelerators in energy and particle intensity has opened the possibility to a wide number of new applications in nuclear technology. This book reviews the applications in the nuclear energy field and the design features of high power neutron sources are explained. Surface treatments of niobium flat samples and superconducting radio frequency cavities by a new technique called gas cluster ion beam are also studied in detail, as well as the process of electropolishing. Furthermore, magnetic devises such as solenoids, dipoles and undulators, which ...

  12. Methodology of Neural Design: Applications in Microwave Engineering

    OpenAIRE

    Z. Raida; P. Pomenka

    2006-01-01

    In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulat...

  13. Three applications of pulse-coupled neural networks

    Science.gov (United States)

    Ranganath, Heggere S.; Banish, Michele R.; Karpinsky, John R.; Clark, Rodney L.; Germany, Glynn A.; Richards, Philip G.

    1999-03-01

    Image segmentation is one of the major application areas for Pulsed Coupled Neural Networks (PCNN). Previous research has shown that the ability of PCNN to ignore minor variations in intensity and small spatial discontinuities in images is beneficial to image segmentation as well as image smoothing. This paper describes research and development projects in progress in which PCNN is used for the segmentation of three different types of digital images. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT (Single Photon Emission Computed Tomography) images of heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global auroral images obtained from Ultraviolet Imager. The paper also describes an hardware implementation of PCNN as an electro-optical chip.

  14. Self-organization in neural networks - Applications in structural optimization

    Science.gov (United States)

    Hajela, Prabhat; Fu, B.; Berke, Laszlo

    1993-01-01

    The present paper discusses the applicability of ART (Adaptive Resonance Theory) networks, and the Hopfield and Elastic networks, in problems of structural analysis and design. A characteristic of these network architectures is the ability to classify patterns presented as inputs into specific categories. The categories may themselves represent distinct procedural solution strategies. The paper shows how this property can be adapted in the structural analysis and design problem. A second application is the use of Hopfield and Elastic networks in optimization problems. Of particular interest are problems characterized by the presence of discrete and integer design variables. The parallel computing architecture that is typical of neural networks is shown to be effective in such problems. Results of preliminary implementations in structural design problems are also included in the paper.

  15. Assessment of Myoelectric Controller Performance and Kinematic Behavior of a Novel Soft Synergy-inspired Robotic Hand for Prosthetic Applications

    Directory of Open Access Journals (Sweden)

    Simone Fani

    2016-10-01

    Full Text Available Myoelectric-artificial limbs can significantly advance the state of the art in prosthetics, since they can be used to control mechatronic devices through muscular activity in a way that mimics how the subjects used to activate their muscles before limb loss. However, surveys indicate that dissatisfaction with the functionality of terminal devices underlies the widespread abandonment of prostheses. We believe that one key factor to improve acceptability of prosthetic devices is to attain human-likeness of prosthesis movements, a goal which is being pursued by research on social and human-robot interactions. Therefore, to reduce early abandonment of terminal devices, we propose that controllers should be designed such as to ensure effective task accomplishment in a natural fashion. In this work, we have analyzed and compared the performance of three types of myoelectric controller algorithms based on surface electromyography to control an under-actuated and multi-degrees of freedom prosthetic hand, the SoftHand Pro. The goal of the present study was to identify the myoelectric algorithm that best mimics the native hand movements. As a preliminary step, we first quantified the repeatability of the SoftHand Pro finger movements and identified the electromyographic recording sites for able-bodied individuals with the highest signal-to-noise ratio from two pairs of muscles, i.e. flexor digitorum superficialis/extensor digitorum communis, and flexor carpi radialis/extensor carpi ulnaris. Able-bodied volunteers were then asked to execute reach-to-grasp movements, while electromyography signals were recorded from flexor digitorum superficialis/extensor digitorum communis as this was identified as the muscle pair characterized by high signal-to-noise ratio and intuitive control. Subsequently, we tested three myoelectric controllers that mapped electromyography signals to position of the SoftHand Pro. We found that a differential electromyography

  16. 24 DOF EMG controlled hybrid actuated prosthetic hand.

    Science.gov (United States)

    Atasoy, A; Kaya, E; Toptas, E; Kuchimov, S; Kaplanoglu, E; Ozkan, M

    2016-08-01

    A complete mechanical design concept of an electromyogram (EMG) controlled hybrid prosthetic hand, with 24 degree of freedom (DOF) anthropomorphic structure is presented. Brushless DC motors along with Shape Memory Alloy (SMA) actuators are used to achieve dexterous functionality. An 8 channel EMG is used for detecting 7 basic hand gestures for control purposes. The prosthetic hand will be integrated with the Neural Network (NNE) based controller in the next phase of the study.

  17. Recurrent Neural Network Applications for Astronomical Time Series

    Science.gov (United States)

    Protopapas, Pavlos

    2017-06-01

    The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

  18. GIANT PROSTHETIC VALVE THROMBUS

    Directory of Open Access Journals (Sweden)

    Prashanth Kumar

    2015-04-01

    Full Text Available Mechanical prosthetic valves are predisposed to bleeding, thrombosis & thromboembolic complications. Overall incidence of thromboembolic complications is 1% per year who are on oral anticoagulants, whereas bleeding complications incidence is 0.5% to 6.6% per year. 1, 2 Minimization of Scylla of thromboembolic & Charybdis of bleeding complication needs a balancing act of optimal antithrombotic therapy. We are reporting a case of middle aged male patient with prosthetic mitral valve presenting in heart failure. Patient had discontinued anticoagulants, as he had subdural hematoma in the past. He presented to our institute with a giant prosthetic valve thrombus.

  19. APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

    Energy Technology Data Exchange (ETDEWEB)

    Musson, John C. [JLAB; Seaton, Chad [JLAB; Spata, Mike F. [JLAB; Yan, Jianxun [JLAB

    2012-11-01

    Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

  20. Practical application of artificial neural networks in the neurosciences

    Science.gov (United States)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  1. Applications of artificial neural networks for microbial water quality modeling

    Energy Technology Data Exchange (ETDEWEB)

    Brion, G.M.; Lingireddy, S. [Univ. of Kentucky, Dept. of Civil Engineering, Lexington, Kentucky (United States)]. E-mail: gbrion@engr.uky.edu

    2002-06-15

    There has been a significant shift in the recent past towards protecting chemical and microbial quality of source waters rather than developing advanced methods to treat heavily polluted water. The key to successful best management practices in protecting the source waters is to identify sources of non-point pollution and their collective impact on the quality of water at the intake. This article presents a few successful applications where artificial neural networks (ANN) have proven to be the useful mathematical tools in correlating the nonlinear relationships between routinely measured parameters (such as rainfall, turbidity, fecal coliforms etc.) and quality of source waters and/or nature of fecal sources. These applications include, prediction of peak concentrations of Giardia and Cryptosporidium, sorting of fecal sources (e.g. agricultural animals vs. urban animals), predicting relative ages of the runoff sources, identifying the potential for sewage contamination. The ability of ANNs to work with complex, inter-related multiparameter databases, and provide superior predictive power in non-linear relationships has been the key for their successful application to microbial water quality studies. (author)

  2. Methodology of Neural Design: Applications in Microwave Engineering

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-06-01

    Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

  3. A new application of neural network technique to sensorless speed identification of induction motor

    OpenAIRE

    Mostefai, Mohamed; Miloud, Yahia; Abdullah MILOUDI

    2016-01-01

    A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasib...

  4. A new application of neural network technique to sensorless speed identification of induction motor

    Directory of Open Access Journals (Sweden)

    Mohamed MOSTEFAI

    2016-12-01

    Full Text Available A new application of neural network technique to sensorless speed identification of scalar-controlled induction motor is implemented in this paper. The neural network estimates the rotor speed through stator measurements and nominal settings of the motor. By changing the motor parameters, the neural network can estimate the speed of another motor. We evaluated our approach based on the speed response and load disturbance effects on two different motors. The test results demonstrate the feasibility of the method.

  5. Application of a Shallow Neural Network to Short-Term Stock Trading

    OpenAIRE

    Madahar, Abhinav; Ma, Yuze; Patel, Kunal

    2017-01-01

    Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's ye...

  6. Neural network and its application to CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Nikravesh, M.; Kovscek, A.R.; Patzek, T.W. [Lawrence Berkeley National Lab., CA (United States)] [and others

    1997-02-01

    We present an integrated approach to imaging the progress of air displacement by spontaneous imbibition of oil into sandstone. We combine Computerized Tomography (CT) scanning and neural network image processing. The main aspects of our approach are (I) visualization of the distribution of oil and air saturation by CT, (II) interpretation of CT scans using neural networks, and (III) reconstruction of 3-D images of oil saturation from the CT scans with a neural network model. Excellent agreement between the actual images and the neural network predictions is found.

  7. Neural networks supporting switching, hypothesis testing, and rule application.

    Science.gov (United States)

    Liu, Zhiya; Braunlich, Kurt; Wehe, Hillary S; Seger, Carol A

    2015-10-01

    We identified dynamic changes in recruitment of neural connectivity networks across three phases of a flexible rule learning and set-shifting task similar to the Wisconsin Card Sort Task: switching, rule learning via hypothesis testing, and rule application. During fMRI scanning, subjects viewed pairs of stimuli that differed across four dimensions (letter, color, size, screen location), chose one stimulus, and received feedback. Subjects were informed that the correct choice was determined by a simple unidimensional rule, for example "choose the blue letter". Once each rule had been learned and correctly applied for 4-7 trials, subjects were cued via either negative feedback or visual cues to switch to learning a new rule. Task performance was divided into three phases: Switching (first trial after receiving the switch cue), hypothesis testing (subsequent trials through the last error trial), and rule application (correct responding after the rule was learned). We used both univariate analysis to characterize activity occurring within specific regions of the brain, and a multivariate method, constrained principal component analysis for fMRI (fMRI-CPCA), to investigate how distributed regions coordinate to subserve different processes. As hypothesized, switching was subserved by a limbic network including the ventral striatum, thalamus, and parahippocampal gyrus, in conjunction with cortical salience network regions including the anterior cingulate and frontoinsular cortex. Activity in the ventral striatum was associated with switching regardless of how switching was cued; visually cued shifts were associated with additional visual cortical activity. After switching, as subjects moved into the hypothesis testing phase, a broad fronto-parietal-striatal network (associated with the cognitive control, dorsal attention, and salience networks) increased in activity. This network was sensitive to rule learning speed, with greater extended activity for the slowest

  8. Advances in Artificial Neural Networks - Methodological Development and Application

    Science.gov (United States)

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  9. Application of a neural network for reflectance spectrum classification

    Science.gov (United States)

    Yang, Gefei; Gartley, Michael

    2017-05-01

    Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

  10. Neural network based adaptive output feedback control: Applications and improvements

    Science.gov (United States)

    Kutay, Ali Turker

    Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

  11. Research on the Application of Artificial Neural Networks in Tender Offer for Construction Projects

    Science.gov (United States)

    Minli, Zhang; Shanshan, Qiao

    The BP model in artificial neural network is used in this paper. Various factors that affect the tender offer is identified and these factors as the input nodes of network to conduct iterated operation in the network is applied in this paper. Through taking advantage of the self-learning function of network, this paper constantly modifies the weight matrix to achieve the objective error of the network error to achieve the function of predicting offer. As a software support tool, MATLAB is used in artificial neural network, the neural network toolbox helps to reduce the workload of writing code greatly and make the application of neural network more widely.

  12. Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

    1995-12-31

    These proceedings contain edited versions of the technical presentations of the Workshop on Environmental and Energy Applications of Neural Networks, held on March 30--31, 1995, in Richland, Washington. The purpose of the workshop was to provide a forum for discussing environmental, energy, and biomedical applications of neural networks. Panels were held to discuss various research and development issues relating to real-world applications in each of the three areas. The applications covered in the workshop were: Environmental applications -- modeling and predicting soil, air and water pollution, environmental sensing, spectroscopy, hazardous waste handling and cleanup; Energy applications -- process monitoring and optimization of power systems, modeling and control of power plants, environmental monitoring for power systems, power load forecasting, fault location and diagnosis of power systems; and Biomedical applications -- medical image and signal analysis, medical diagnosis, analysis of environmental health effects, and modeling biological systems. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database.

  13. Polypyrrole-coated electrospun PLGA nanofibers for neural tissue applications.

    Science.gov (United States)

    Lee, Jae Y; Bashur, Chris A; Goldstein, Aaron S; Schmidt, Christine E

    2009-09-01

    Electrospinning is a promising approach to create nanofiber structures that are capable of supporting adhesion and guiding extension of neurons for nerve regeneration. Concurrently, electrical stimulation of neurons in the absence of topographical features also has been shown to guide axonal extension. Therefore, the goal of this study was to form electrically conductive nanofiber structures and to examine the combined effect of nanofiber structures and electrical stimulation. Conductive meshes were produced by growing polypyrrole (PPy) on random and aligned electrospun poly(lactic-co-glycolic acid) (PLGA) nanofibers, as confirmed by scanning electron micrographs and X-ray photon spectroscopy. PPy-PLGA electrospun meshes supported the growth and differentiation of rat pheochromocytoma 12 (PC12) cells and hippocampal neurons comparable to non-coated PLGA control meshes, suggesting that PPy-PLGA may be suitable as conductive nanofibers for neuronal tissue scaffolds. Electrical stimulation studies showed that PC12 cells, stimulated with a potential of 10 mV/cm on PPy-PLGA scaffolds, exhibited 40-50% longer neurites and 40-90% more neurite formation compared to unstimulated cells on the same scaffolds. In addition, stimulation of the cells on aligned PPy-PLGA fibers resulted in longer neurites and more neurite-bearing cells than stimulation on random PPy-PLGA fibers, suggesting a combined effect of electrical stimulation and topographical guidance and the potential use of these scaffolds for neural tissue applications.

  14. Polypyrrole-Coated Electrospun PLGA Nanofibers for Neural Tissue Applications

    Science.gov (United States)

    Lee, Jae Young; Bashur, Chris A.; Goldstein, Aaron S.; Schmidt, Christine E.

    2009-01-01

    Electrospinning is a promising approach to create nanofiber structures that are capable of supporting adhesion and guiding extension of neurons for nerve regeneration. Concurrently, electrical stimulation of neurons in the absence of topographical features also has been shown to guide axonal extension. Therefore, the goal of this study was to form electrically conductive nanofiber structures and to examine the combined effect of nanofiber structures and electrical stimulation. Conductive meshes were produced by growing polypyrrole (PPy) on random and aligned electrospun poly(lactic-co-glycolic acid) (PLGA) nanofibers, as confirmed by scanning electron micrographs and X-ray photon spectroscopy. PPy-PLGA electrospun meshes supported the growth and differentiation of rat pheochromocytoma 12 (PC12) cells and hippocampal neurons comparable to non-coated PLGA control meshes, suggesting that PPy-PLGA may be suitable as conductive nanofibers for neuronal tissue scaffolds. Electrical stimulation studies showed that PC12 cells, stimulated with a potential of 10 mV/cm on PPy-PLGA scaffolds, exhibited 40–50% longer neurites and 40–90% more neurite formation compared to unstimulated cells on the same scaffolds. In addition, stimulation of the cells on aligned PPy-PLGA fibers resulted in longer neurites and more neurite-bearing cells than stimulation on random PPy-PLGA fibers, suggesting a combined effect of electrical stimulation and topographical guidance and the potential use of these scaffolds for neural tissue applications. PMID:19501901

  15. Chaotic Hopfield Neural Network Swarm Optimization and Its Application

    Directory of Open Access Journals (Sweden)

    Yanxia Sun

    2013-01-01

    Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.

  16. FPGA platform for prototyping and evaluation of neural network automotive applications

    Science.gov (United States)

    Aranki, N.; Tawel, R.

    2002-01-01

    In this paper we present an FPGA based reconfigurable computing platform for prototyping and evaluation of advanced neural network based applications for control and diagnostics in an automotive sub-systems.

  17. Application of neural networks in coastal engineering - An overview

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.

    . International conference on COPEDEC VII, Dubai (UAE), paper no- 27, 1-11. Mandal, S. 2001. Tides prediction using back propagation neural networks, Proc. International Conference in Ocean Engineering, ICOE, IIT Madras, 499-504. Mandal, S. and Prabhaharan, N...

  18. The Component Timed-Up-and-Go test: the utility and psychometric properties of using a mobile application to determine prosthetic mobility in people with lower limb amputations.

    Science.gov (United States)

    Clemens, Sheila M; Gailey, Robert S; Bennett, Christopher L; Pasquina, Paul F; Kirk-Sanchez, Neva J; Gaunaurd, Ignacio A

    2017-08-01

    Using a custom mobile application to evaluate the reliability and validity of the Component Timed-Up-and-Go test to assess prosthetic mobility in people with lower limb amputation. Cross-sectional design. National conference for people with limb loss. A total of 118 people with non-vascular cause of lower limb amputation participated. Subjects had a mean age of 48 (±13.7) years and were an average of 10 years post amputation. Of them, 54% ( n = 64) of subjects were male. None. The Component Timed-Up-and-Go was administered using a mobile iPad application, generating a total time to complete the test and five component times capturing each subtask (sit to stand transitions, linear gait, turning) of the standard timed-up-and-go test. The outcome underwent test-retest reliability using intraclass correlation coefficients (ICCs) and convergent validity analyses through correlation with self-report measures of balance and mobility. The Component Timed-Up-and-Go exhibited excellent test-retest reliability with ICCs ranging from .98 to .86 for total and component times. Evidence of discriminative validity resulted from significant differences in mean total times between people with transtibial (10.1 (SD: ±2.3)) and transfemoral (12.76 (SD: ±5.1) amputation, as well as significant differences in all five component times ( P mobility in people with non-vascular lower limb amputation. The iPad application provided a means to easily record data, contributing to clinical utility.

  19. Neural Networks and Their Application to Air Force Personnel Modeling

    Science.gov (United States)

    1991-11-01

    breadth of techniques provides fertile ground against which to compare the results obtained with neural networks. ", Most of the models in reenlistment or...Specialties (MOSs) receiving SRBs were taken from the 1980 and 1981 Enlisted Master Files ( EMFs ). These 98 MOSs were then aggregated into 15 Career Management... mechanisms , and architectures. Neural Networks, 1(1), 17-62. Hagiwara, M. (1990). Accelerated backpropagation using unlearning based on a Hebb rule

  20. A Fast C++ Implementation of Neural Network Backpropagation Training Algorithm: Application to Bayesian Optimal Image Demosaicing

    Directory of Open Access Journals (Sweden)

    Yi-Qing Wang

    2015-09-01

    Full Text Available Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(· is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing.

  1. Neural Network Methodology for Earthquake Early Warning - first applications

    Science.gov (United States)

    Wenzel, F.; Koehler, N.; Cua, G.; Boese, M.

    2007-12-01

    PreSEIS is a method for earthquake early warning for finite faults (Böse, 2006) that is based on Artificial Neural Networks (ANN's), which are used for the mapping of seismic observations onto likely source parameters, including the moment magnitude and the location of an earthquake. PreSEIS integrates all available information on ground shaking at different sensors in a seismic network and up-dates the estimates of seismic source parameters regularly with proceeding time. PreSEIS has been developed and tested with synthetic waveform data using the example of Istanbul, Turkey (Böse, 2006). We will present first results of the application of PreSEIS to real data from Southern California, recorded at stations from the Southern California Seismic Network. The dataset consists of 69 shallow local earthquakes with moment magnitudes ranging between 1.96 and 7.1. The data come from broadband (20 or 40 Hz) or high broadband (80 or 100 Hz), high gain channels (3-component). The Southern California dataset will allow a comparison of our results to those of the Virtual Seismologist (Cua, 2004). We used the envelopes of the waveforms defined by Cua (2004) as input for the ANN's. The envelopes were obtained by taking the maximum absolute amplitude value of the recorded ground motion time history over a 1-second time window. Due to the fact that not all of the considered stations have recorded each earthquake, the missing records were replaced by synthetic envelopes, calculated by envelope attenuation relationships developed by Cua (2004).

  2. Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

  3. Rotationally Actuated Prosthetic Hand

    Science.gov (United States)

    Norton, William E.; Belcher, Jewell G., Jr.; Carden, James R.; Vest, Thomas W.

    1991-01-01

    Prosthetic hand attached to end of remaining part of forearm and to upper arm just above elbow. Pincerlike fingers pushed apart to degree depending on rotation of forearm. Simpler in design, simpler to operate, weighs less, and takes up less space.

  4. Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

    Science.gov (United States)

    Gonzalez-Vargas, Jose; Dosen, Strahinja; Amsuess, Sebastian; Yu, Wenwei; Farina, Dario

    2015-01-01

    Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI) cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns) and/or the user has a considerable impairment (limited number of available signal sources). In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate), decoding (one signal to recognize), and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair), or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces) in order to improve the usability of existing low-bandwidth HMIs.

  5. Human-Machine Interface for the Control of Multi-Function Systems Based on Electrocutaneous Menu: Application to Multi-Grasp Prosthetic Hands.

    Directory of Open Access Journals (Sweden)

    Jose Gonzalez-Vargas

    Full Text Available Modern assistive devices are very sophisticated systems with multiple degrees of freedom. However, an effective and user-friendly control of these systems is still an open problem since conventional human-machine interfaces (HMI cannot easily accommodate the system's complexity. In HMIs, the user is responsible for generating unique patterns of command signals directly triggering the device functions. This approach can be difficult to implement when there are many functions (necessitating many command patterns and/or the user has a considerable impairment (limited number of available signal sources. In this study, we propose a novel concept for a general-purpose HMI where the controller and the user communicate bidirectionally to select the desired function. The system first presents possible choices to the user via electro-tactile stimulation; the user then acknowledges the desired choice by generating a single command signal. Therefore, the proposed approach simplifies the user communication interface (one signal to generate, decoding (one signal to recognize, and allows selecting from a number of options. To demonstrate the new concept the method was used in one particular application, namely, to implement the control of all the relevant functions in a state of the art commercial prosthetic hand without using any myoelectric channels. We performed experiments in healthy subjects and with one amputee to test the feasibility of the novel approach. The results showed that the performance of the novel HMI concept was comparable or, for some outcome measures, better than the classic myoelectric interfaces. The presented approach has a general applicability and the obtained results point out that it could be used to operate various assistive systems (e.g., prosthesis vs. wheelchair, or it could be integrated into other control schemes (e.g., myoelectric control, brain-machine interfaces in order to improve the usability of existing low

  6. A proposed model of the response of the anophthalmic socket to prosthetic eye wear and its application to the management of mucoid discharge.

    Science.gov (United States)

    Pine, Keith R; Sloan, Brian H; Jacobs, Robert J

    2013-08-01

    Mucoid discharge associated with prosthetic eye wear can be a distressing condition that affects the quality of life of people who have lost an eye. Discharge is the second highest concern of experienced prosthetic eye wearers after health of the companion eye and is prevalent in anophthalmic populations. Specific causes of mucoid discharge such as infections and environmental allergens are well understood, but non-specific causes are unknown and an evidence based protocol for managing non-specific discharge is lacking. Current management is based on prosthesis removal and cleaning, and professional re-polishing of the prosthesis. Tear protein deposits accumulate on prosthetic eyes. These deposits mediate the response of the socket to prosthetic eye wear and their influence (good and bad) is determined by differing cleaning regimes and standards of surface finish. This paper proposes a three-phase model that describes the response of the socket to prosthetic eye wear. The phases are: An initial period of wear of a new (or newly-polished) prosthesis when homeostasis is being established (or re-established) within the socket; a second period (equilibrium phase) where beneficial surface deposits have built up on the prosthesis and wear is safe and comfortable, and a third period (breakdown phase) where there is an increasing likelihood of harm from continued wear. The proposed model provides a rationale for a personal cleaning regime to manage non-specific mucoid discharge. Professional care of prosthetic eyes is also important for the management of discharge and evidence for effective surface finishing is reported in this study. Taken together, the proposed regimes for personal and professional care comprise a protocol for managing discharge associated with prosthetic eye wear. The protocol describes prosthetic eye cleaning methods and frequency, and suggests minimum standards for professional polishing. If confirmed, the protocol has the potential to resolve the

  7. A bio-inspired force control for cyclic manipulation of prosthetic hands.

    Science.gov (United States)

    Ciancio, A L; Barone, R; Zollo, L; Carpino, G; Davalli, A; Sacchetti, R; Guglielmelli, E

    2015-01-01

    The human hand is considered as the highest example of dexterous system capable of interacting with different objects and adapting its manipulation abilities to them. The control of poliarticulated prosthetic hands represents one important research challenge, typically aiming at replicating the manipulation capabilities of the natural hand. For this reason, this paper wants to propose a bio-inspired learning architecture based on parallel force/position control for prosthetic hands, capable of learning cyclic manipulation capabilities. To this purpose, it is focused on the control of a commercial biomechatronic hand (the IH2 hand) including the main features of recent poliarticulated prosthetic hands. The training phase of the hand was carried out in simulation, the parallel force/position control was tested in simulation whereas preliminary tests were performed on the real IH2 hand. The results obtained in simulation and on the real hand provide an important evidence of the applicability of the bio-inspired neural control to real biomechatronic hand with the typical features of a hand prosthesis.

  8. A novel neural dynamical approach to convex quadratic program and its efficient applications.

    Science.gov (United States)

    Xia, Youshen; Sun, Changyin

    2009-12-01

    This paper proposes a novel neural dynamical approach to a class of convex quadratic programming problems where the number of variables is larger than the number of equality constraints. The proposed continuous-time and proposed discrete-time neural dynamical approach are guaranteed to be globally convergent to an optimal solution. Moreover, the number of its neurons is equal to the number of equality constraints. In contrast, the number of neurons in existing neural dynamical methods is at least the number of the variables. Therefore, the proposed neural dynamical approach has a low computational complexity. Compared with conventional numerical optimization methods, the proposed discrete-time neural dynamical approach reduces multiplication operation per iteration and has a large computational step length. Computational examples and two efficient applications to signal processing and robot control further confirm the good performance of the proposed approach.

  9. Statistical modelling of neural networks in {gamma}-spectrometry applications

    Energy Technology Data Exchange (ETDEWEB)

    Vigneron, V.; Martinez, J.M. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. de Mecanique et de Technologie; Morel, J.; Lepy, M.C. [CEA Centre d`Etudes de Saclay, 91 - Gif-sur-Yvette (France). Dept. des Applications et de la Metrologie des Rayonnements Ionisants

    1995-12-31

    Layered Neural Networks, which are a class of models based on neural computation, are applied to the measurement of uranium enrichment, i.e. the isotope ratio {sup 235} U/({sup 235} U + {sup 236} U + {sup 238} U). The usual method consider a limited number of {Gamma}-ray and X-ray peaks, and require previously calibrated instrumentation for each sample. But, in practice, the source-detector ensemble geometry conditions are critically different, thus a means of improving the above convention methods is to reduce the region of interest: this is possible by focusing on the K{sub {alpha}} X region where the three elementary components are present. Real data are used to study the performance of neural networks. Training is done with a Maximum Likelihood method to measure uranium {sup 235} U and {sup 238} U quantities in infinitely thick samples. (authors). 18 refs., 6 figs., 3 tabs.

  10. Application of design of experiments and artificial neural networks ...

    African Journals Online (AJOL)

    user

    Abstract. This paper discusses the use of Distance based optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in optimizing the stacking sequence for simply supported laminated composite plate under uniformly distributed load (UDL) for minimizing the deflections and stresses. A number ...

  11. Applicability of tooth derived stem cells in neural regeneration

    Directory of Open Access Journals (Sweden)

    Ludovica Parisi

    2016-01-01

    Full Text Available Within the nervous system, regeneration is limited, and this is due to the small amount of neural stem cells, the inhibitory origin of the stem cell niche and often to the development of a scar which constitutes a mechanical barrier for the regeneration. Regarding these aspects, many efforts have been done in the research of a cell component that combined with scaffolds and growth factors could be suitable for nervous regeneration in regenerative medicine approaches. Autologous mesenchymal stem cells represent nowadays the ideal candidate for this aim, thank to their multipotency and to their amount inside adult tissues. However, issues in their harvesting, through the use of invasive techniques, and problems involved in their ageing, require the research of new autologous sources. To this purpose, the recent discovery of a stem cells component in teeth, and which derive from neural crest cells, has came to the light the possibility of using dental stem cells in nervous system regeneration. In this work, in order to give guidelines on the use of dental stem cells for neural regeneration, we briefly introduce the concepts of regeneration and regenerative medicine, we then focus the attention on odontogenesis, which involves the formation and the presence of a stem component in different parts of teeth, and finally we describe some experimental approaches which are exploiting dental stem cells for neural studies.

  12. Application of design of experiments and artificial neural networks ...

    African Journals Online (AJOL)

    This paper discusses the use of Distance based optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in optimizing the stacking sequence for simply supported laminated composite plate under uniformly distributed load (UDL) for minimizing the deflections and stresses. A number of finite ...

  13. Image Filtering with Neural Networks: applications and performance evaluation

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan

    1992-01-01

    A simple and elegant method to design image filters with neural networks is proposed: using small networks that scan the image and perform position invariant filtering. In the theses examples of image filtering with error backpropagation networks for edge detection, image deblurring and noise

  14. Applicability of tooth derived stem cells in neural regeneration.

    Science.gov (United States)

    Parisi, Ludovica; Manfredi, Edoardo

    2016-11-01

    Within the nervous system, regeneration is limited, and this is due to the small amount of neural stem cells, the inhibitory origin of the stem cell niche and often to the development of a scar which constitutes a mechanical barrier for the regeneration. Regarding these aspects, many efforts have been done in the research of a cell component that combined with scaffolds and growth factors could be suitable for nervous regeneration in regenerative medicine approaches. Autologous mesenchymal stem cells represent nowadays the ideal candidate for this aim, thank to their multipotency and to their amount inside adult tissues. However, issues in their harvesting, through the use of invasive techniques, and problems involved in their ageing, require the research of new autologous sources. To this purpose, the recent discovery of a stem cells component in teeth, and which derive from neural crest cells, has came to the light the possibility of using dental stem cells in nervous system regeneration. In this work, in order to give guidelines on the use of dental stem cells for neural regeneration, we briefly introduce the concepts of regeneration and regenerative medicine, we then focus the attention on odontogenesis, which involves the formation and the presence of a stem component in different parts of teeth, and finally we describe some experimental approaches which are exploiting dental stem cells for neural studies.

  15. Welding of Prosthetic Alloys

    Directory of Open Access Journals (Sweden)

    Wojciechowska M.

    2015-04-01

    Full Text Available This paper presents the techniques of joining metal denture elements, used in prosthetic dentistry: the traditional soldering technique with a gas burner and a new technique of welding with a laser beam; the aim of the study was to make a comparative assessment of the quality of the joints in view of the possibility of applying them in prosthetic structures. Fractographic examinations were conducted along with tensile strength and impact strength tests, and the quality of the joints was assessed compared to the solid metal. The experiments have shown that the metal elements used to make dentures, joined by the technique which employs a laser beam, have better strength properties than those achieved with a gas burner.

  16. Application of Neural Network and Simulation Modeling to Evaluate Russian Banks’ Performance

    OpenAIRE

    Sharma, Satish; Shebalkov, Mikhail

    2013-01-01

    This paper presents an application of neural network and simulation modeling to analyze and predict the performance of 883 Russian Banks over the period 2000-2010. Correlation analysis was performed to obtain key financial indicators which reflect the leverage, liquidity, profitability and size of Banks. Neural network was trained over the entire dataset, and then simulation modeling was performed generating values which are distributed with Largest Extreme Value and Loglogistic distributions...

  17. Comparison of Simulation Algorithms for the Hopfield Neural Network: An Application of Economic Dispatch

    OpenAIRE

    Altun, Tankut Yalçınöz and Halis

    2014-01-01

    This paper is mainly concerned with an investigation of the suitability of Hopfield neural network structures in solving the power economic dispatch problem. For Hopfield neural network applications to this problem three important questions have been answered: what the size of the power system is; how efficient the computational method; and how to handle constraints. A new mapping process is formulated and a computational method for obtaining the weights and biases is described. A few simulat...

  18. A critical review on the applications of artificial neural networks in winemaking technology.

    Science.gov (United States)

    Moldes, O A; Mejuto, J C; Rial-Otero, R; Simal-Gandara, J

    2017-09-02

    Since their development in 1943, artificial neural networks were extended into applications in many fields. Last twenty years have brought their introduction into winery, where they were applied following four basic purposes: authenticity assurance systems, electronic sensory devices, production optimization methods, and artificial vision in image treatment tools, with successful and promising results. This work reviews the most significant approaches for neural networks in winemaking technologies with the aim of producing a clear and useful review document.

  19. Synthesize, optimize, analyze, repeat (SOAR): Application of neural network tools to ECG patient monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Watrous, R.; Towell, G.; Glassman, M.S. [Siemens Corporate Research, Princeton, NJ (United States)

    1995-12-31

    Results are reported from the application of tools for synthesizing, optimizing and analyzing neural networks to an ECG Patient Monitoring task. A neural network was synthesized from a rule-based classifier and optimized over a set of normal and abnormal heartbeats. The classification error rate on a separate and larger test set was reduced by a factor of 2. When the network was analyzed and reduced in size by a factor of 40%, the same level of performance was maintained.

  20. [Artificial neural networks and their application in drug addiction medicine].

    Science.gov (United States)

    Buscema, P M

    2000-01-01

    This article presents the use of a new processing technology: artificial neural networks (ANN) modeling. We introduce the reader, in an easy way, to types of problems for which researchers can use this technology. ANN represents a powerful way for understanding, predicting, and simulating complex and chaotic dynamics. We believe that social fields, human psychology, and behaviors, which are nonlinear in their essence, need an adequate methodology such as ANN in order to best investigate and comprehend them.

  1. Artificial neural networks application for solid fuel slagging intensity predictions

    Directory of Open Access Journals (Sweden)

    Kakietek Sławomir

    2017-01-01

    Full Text Available Slagging issues present in pulverized steam boilers very often lead to heat transfer problems, corrosion and not planned outages of boilers which increase the cost of energy production and decrease the efficiency of energy production. Slagging especially occurs in regions with reductive atmospheres which nowadays are very common due to very strict limitations in NOx emissions. Moreover alternative fuels like biomass which are also used in combustion systems from two decades in order to decrease CO2 emissions also usually increase the risk of slagging. Thus the prediction of slagging properties of fuels is not the minor issue which can be neglected before purchasing or mixing of fuels. This however is rather difficult to estimate and even commonly known standard laboratory methods like fusion temperature determination or special indexers calculated on the basis of proximate and ultimate analyses, very often have no reasonable correlation to real boiler fuel behaviour. In this paper the method of determination of slagging properties of solid fuels based on laboratory investigation and artificial neural networks were presented. A fuel data base with over 40 fuels was created. Neural networks simulations were carried out in order to predict the beginning temperature and intensity of slagging. Reasonable results were obtained for some of tested neural networks, especially for hybrid feedforward networks with PCA technique. Consequently neural network model will be used in Common Intelligent Boiler Operation Platform (CIBOP being elaborated within CERUBIS research project for two BP-1150 and BB-1150 steam boilers. The model among others enables proper fuel selection in order to minimize slagging risk.

  2. Application of Artificial Neural Networks for Predicting Generated Wind Power

    OpenAIRE

    Vijendra Singh

    2016-01-01

    This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, gener...

  3. Application of neural networks to waste site screening

    Energy Technology Data Exchange (ETDEWEB)

    Dabiri, A.E.; Kraft, T.; Hilton, J.M. [Science Applications International Corp., San Diego, CA (United States)

    1993-03-01

    Waste site screening requires knowledge of the actual concentrations of hazardous materials and rates of flow around and below the site with time. The present approach to site screening consists primarily of drilling, boreholes near contaminated site and chemically analyzing the extracted physical samples and processing the data. In addition, hydraulic and geochemical soil properties are obtained so that numerical simulation models can be used to interpret and extrapolate the field data. The objective of this work is to investigate the feasibility of using neural network techniques to reduce the cost of waste site screening. A successful technique may lead to an ability to reduce the number of boreholes and the number of samples analyzed from each borehole to properly screen the waste site. The analytic tool development described here is inexpensive because it makes use of neural network techniques that can interpolate rapidly and which can learn how to analyze data rather than having to be explicitly programmed. In the following sections, data collection and data analyses will be described, followed by a section on different neural network techniques used. The results will be presented and compared with mathematical model. Finally, the last section will summarize the research work performed and make several recommendations for future work.

  4. Computing with Biologically Inspired Neural Oscillators: Application to Colour Image Segmentation

    Directory of Open Access Journals (Sweden)

    Ammar Belatreche

    2010-01-01

    Full Text Available This paper investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that exploits the synergy between neural oscillators and Kohonen self-organising maps (SOMs is presented. It consists of a two-dimensional grid of neural oscillators which are locally connected through excitatory connections and globally connected to a common inhibitor. Each neuron is mapped to a pixel of the input image and existing objects, represented by homogenous areas, are temporally segmented through synchronisation of the activity of neural oscillators that are mapped to pixels of the same object. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators for temporal correlation-based object segmentation. Both chromatic and local spatial features are used. The system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bioinspired approach for colour image segmentation. The paper concludes with a discussion of the performance of the proposed system and its comparison with traditional image segmentation approaches.

  5. Application of Neural Network to Determine the Source Location in Acoustic Emission

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Eun [Sambo Engineering Co, Seoul (Korea, Republic of)

    2005-12-15

    The iterative calculation by least square method was used to determine the source location of acoustic emission in rock, as so called 'traditional method'. The results were compared with source coordinates inferred from the application of neural network system for new input data, as so called 'new method'. Input data of the neural network were based on the time differences of longitudinal waves arrived from acoustic emission events at each transducer, the variation of longitudinal velocities at each stress level, and the coordinates of transducer as in the traditional method. The momentum back propagation neural network system adopted to determine source location, which consists of three layers, and has twenty-seven input processing elements. Applicability of the new method were identified, since the results of source location by the application of two methods were similarly concordant

  6. Viability of Controlling Prosthetic Hand Utilizing Electroencephalograph (EEG) Dataset Signal

    Science.gov (United States)

    Miskon, Azizi; A/L Thanakodi, Suresh; Raihan Mazlan, Mohd; Mohd Haziq Azhar, Satria; Nooraya Mohd Tawil, Siti

    2016-11-01

    This project presents the development of an artificial hand controlled by Electroencephalograph (EEG) signal datasets for the prosthetic application. The EEG signal datasets were used as to improvise the way to control the prosthetic hand compared to the Electromyograph (EMG). The EMG has disadvantages to a person, who has not used the muscle for a long time and also to person with degenerative issues due to age factor. Thus, the EEG datasets found to be an alternative for EMG. The datasets used in this work were taken from Brain Computer Interface (BCI) Project. The datasets were already classified for open, close and combined movement operations. It served the purpose as an input to control the prosthetic hand by using an Interface system between Microsoft Visual Studio and Arduino. The obtained results reveal the prosthetic hand to be more efficient and faster in response to the EEG datasets with an additional LiPo (Lithium Polymer) battery attached to the prosthetic. Some limitations were also identified in terms of the hand movements, weight of the prosthetic, and the suggestions to improve were concluded in this paper. Overall, the objective of this paper were achieved when the prosthetic hand found to be feasible in operation utilizing the EEG datasets.

  7. Practical Application of Neural Networks in State Space Control

    DEFF Research Database (Denmark)

    Bendtsen, Jan Dimon

    theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train....... Then the controller is shown to work on a simulation example. We also address the potential problem of too rapidly fluctuating parameters by including regularization in the learning rule. Next we develop a direct adaptive certainty-equivalence controller based on neurofuzzy models. The control loop is proven...

  8. Prosthetic elbow joint

    Science.gov (United States)

    Weddendorf, Bruce C. (Inventor)

    1994-01-01

    An artificial, manually positionable elbow joint for use in an upper extremity, above-elbow, prosthetic is described. The prosthesis provides a locking feature that is easily controlled by the wearer. The instant elbow joint is very strong and durable enough to withstand the repeated heavy loadings encountered by a wearer who works in an industrial, construction, farming, or similar environment. The elbow joint of the present invention comprises a turntable, a frame, a forearm, and a locking assembly. The frame generally includes a housing for the locking assembly and two protruding ears. The forearm includes an elongated beam having a cup-shaped cylindrical member at one end and a locking wheel having a plurality of holes along a circular arc on its other end with a central bore for pivotal attachment to the protruding ears of the frame. The locking assembly includes a collar having a central opening with a plurality of internal grooves, a plurality of internal cam members each having a chamfered surface at one end and a V-shaped slot at its other end; an elongated locking pin having a crown wheel with cam surfaces and locking lugs secured thereto; two coiled compression springs; and a flexible filament attached to one end of the elongated locking pin and extending from the locking assembly for extending and retracting the locking pin into the holes in the locking wheel to permit selective adjustment of the forearm relative to the frame. In use, the turntable is affixed to the upper arm part of the prosthetic in the conventional manner, and the cup-shaped cylindrical member on one end of the forearm is affixed to the forearm piece of the prosthetic in the conventional manner. The elbow joint is easily adjusted and locked between maximum flex and extended positions.

  9. Artificial neural network applications in the calibration of spark-ignition engines: An overview

    Directory of Open Access Journals (Sweden)

    Richard Fiifi Turkson

    2016-09-01

    Full Text Available Emission legislation has become progressively tighter, making the development of new internal combustion engines very challenging. New engine technologies for complying with these regulations introduce an exponential dependency between the number of test combinations required for obtaining optimum results and the time and cost outlays. This makes the calibration task very expensive and virtually impossible to carry out. The potential use of trained neural networks in combination with Design of Experiments (DoE methods for engine calibration has been a subject of research activities in recent times. This is because artificial neural networks, compared with other data-driven modeling techniques, perform better in satisfying a majority of the modeling requirements for engine calibration including the curse of dimensionality; the use of DoE for obtaining few measurements as practicable, with the aim of reducing engine calibration costs; the required flexibility that allows model parameters to be optimized to avoid overfitting; and the facilitation of automated online optimization during the engine calibration process that eliminates the need for user intervention. The purpose of this review is to give an overview of the various applications of neural networks in the calibration of spark-ignition engines. The identified and discussed applications include system identification for rapid prototyping, virtual sensing, use of neural networks as look-up table surrogates, emerging control strategies and On-Board Diagnostic (OBD applications. The demerits of neural networks, future possibilities and alternatives were also discussed.

  10. Color segmentation using neural networks: an application to dermatology

    Science.gov (United States)

    Yova, Dido M.; Delibasis, Athanasios K.; Papaodysseus, Constantinos N.

    1999-02-01

    In the incidence of malignant melanoma, which is the most lethal skin cancer, has risen around the world more than 15 times over the last 50 years and continues to increase. Diagnosis of skin tumors can be automated based on the introduction of digital imaging in dermatology. Automated diagnosis is based on certain physical features and color information that are characteristic of benign, dysplastic or malignant tissue. In this paper, the research is addressed towards the problem of segmentation of digital images based on color information and specifically was a selected feature called variegated coloring. Neural networks - Kohonen model - were used for the automatic identification of variegated coloring and self organizing maps (SOMs) were applied to the segmentation of color images of skin cancer. A set of 12 images was used and the results were compared with the segmentation procedure of a clinical expert. The results have shown that the Kohonen model of neural networks can utilize the chromatic information of color skin images to successfully segment skin cancers from the surrounding skin.

  11. Multifunctional nanowire scaffolds for neural tissue engineering applications

    Science.gov (United States)

    Bechara, Samuel Leo

    Unlike other regions of the body, the nervous system is extremely vulnerable to damage and injury because it has a limited ability to self-repair. Over 250,000 people in the United States have spinal cord injuries and due to the complicated pathophysiology of such injuries, there are few options available for functional regeneration of the spinal column. Furthermore, peripheral nerve damage is troublingly common in the United States, with an estimated 200,000 patients treated surgically each year. The current gold standard in treatment for peripheral nerve damage is a nerve autograft. This technique was pioneered over 45 years ago, but suffers from a major drawback. By transecting a nerve from another part of the body, function is regained at the expense of destroying a nerve connection elsewhere. Because of these issues, the investigation of different materials for regenerating nervous tissue is necessary. This work examines multi-functional nanowire scaffolds to provide physical and chemical guidance cues to neural stem cells to enhance cellular activity from a biomedical engineering perspective. These multi-functional scaffolds include a unique nanowire nano-topography to provide physical cues to guide cellular adhesion. The nanowires were then coated with an electrically conductive polymer to further enhance cellular activity. Finally, nerve growth factor was conjugated to the surface of the scaffolds to provide chemical cues for the neural stem cells. The results in this work suggest that these multifunctional nanowire scaffolds could be used in vivo to repair nervous system tissue.

  12. Image Finder Mobile Application Based on Neural Networks

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2017-04-01

    Full Text Available Nowadays taking photos via mobile phone has become a very important part of everyone’s life. Almost each and every person who has a smart phone also has thousands of photos in their mobile device. At times it becomes very difficult to find a particular photo from thousands of photos, and it takes time. This research was done to come up with an innovative solution that could solve this problem. The solution will allow the user to find the required photo by simply drawing a sketch on the objects in the required picture, for example a tree or car, etc. Two types of supervised Artificial Neural Networks are used for this purpose; one is trained to identify the handmade sketches and other is trained to identify the images. The proposed approach introduces a mechanism to relate the sketches with the images by matching them after training. The experimentation results for testing the trained neural networks reached 100% for the sketches, and 84% for the images of two objects as a case study.

  13. Application of Artificial Neural Networks for Catalysis: A Review

    National Research Council Canada - National Science Library

    Hao Li; Zhien Zhang; Zhijian Liu

    2017-01-01

    .... However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature...

  14. Application of artificial neural networks in computer-aided diagnosis.

    Science.gov (United States)

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  15. Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

    Science.gov (United States)

    Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

    2015-01-01

    Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

  16. Innovations in prosthetic interfaces for the upper extremity.

    Science.gov (United States)

    Kung, Theodore A; Bueno, Reuben A; Alkhalefah, Ghadah K; Langhals, Nicholas B; Urbanchek, Melanie G; Cederna, Paul S

    2013-12-01

    Advancements in modern robotic technology have led to the development of highly sophisticated upper extremity prosthetic limbs. High-fidelity volitional control of these devices is dependent on the critical interface between the patient and the mechanical prosthesis. Recent innovations in prosthetic interfaces have focused on several control strategies. Targeted muscle reinnervation is currently the most immediately applicable prosthetic control strategy and is particularly indicated in proximal upper extremity amputations. Investigation into various brain interfaces has allowed acquisition of neuroelectric signals directly or indirectly from the central nervous system for prosthetic control. Peripheral nerve interfaces permit signal transduction from both motor and sensory nerves with a higher degree of selectivity. This article reviews the current developments in each of these interface systems and discusses the potential of these approaches to facilitate motor control and sensory feedback in upper extremity neuroprosthetic devices.

  17. Absolute stability of nonlinear systems with time delays and applications to neural networks

    Directory of Open Access Journals (Sweden)

    Xinzhi Liu

    2001-01-01

    Full Text Available In this paper, absolute stability of nonlinear systems with time delays is investigated. Sufficient conditions on absolute stability are derived by using the comparison principle and differential inequalities. These conditions are simple and easy to check. In addition, exponential stability conditions for some special cases of nonlinear delay systems are discussed. Applications of those results to cellular neural networks are presented.

  18. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  19. Abstracts for the symposium on the Application of neural networks to the earth sciences

    Science.gov (United States)

    Singer, Donald A.

    2002-01-01

    Artificial neural networks are a group of mathematical methods that attempt to mimic some of the processes in the human mind. Although the foundations for these ideas were laid as early as 1943 (McCulloch and Pitts, 1943), it wasn't until 1986 (Rumelhart and McClelland, 1986; Masters, 1995) that applications to practical problems became possible. It is the acknowledged superiority of the human mind at recognizing patterns that the artificial neural networks are trying to imitate with their interconnected neurons. Interconnections used in the methods that have been developed allow robust learning. Capabilities of neural networks fall into three kinds of applications: (1) function fitting or prediction, (2) noise reduction or pattern recognition, and (3) classification or placing into types. Because of these capabilities and the powerful abilities of artificial neural networks, there have been increasing applications of these methods in the earth sciences. The abstracts in this document represent excellent samples of the range of applications. Talks associated with the abstracts were presented at the Symposium on the Application of Neural Networks to the Earth Sciences: Seventh International Symposium on Mineral Exploration (ISME–02), held August 20–21, 2002, at NASA Moffett Field, Mountain View, California. This symposium was sponsored by the Mining and Materials Processing Institute of Japan (MMIJ), the U.S. Geological Survey, the Circum-Pacific Council, and NASA. The ISME symposia have been held every two years in order to bring together scientists actively working on diverse quantitative methods applied to the earth sciences. Although the title, International Symposium on Mineral Exploration, suggests exclusive focus on mineral exploration, interests and presentations have always been wide-ranging—abstracts presented here are no exception.

  20. Application of recurrent neural networks for drought projections in California

    Science.gov (United States)

    Le, J. A.; El-Askary, H. M.; Allali, M.; Struppa, D. C.

    2017-05-01

    We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions.

  1. AN APPLICATION OF SPEAKER RECOGNITION USING ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Murat CANER

    2006-02-01

    Full Text Available In this study an artificial neural network (ANN is implemented, which has been used frequently as an implementation model in recent years, to recognize speaker identification. Generally, recognition is consist of three stages that, processing of signal, obtaining attributes and comparing them. Speech samples are transformed into digital data according to voice card of PC. In the analysis of voice stage, recurrent periods and white noise of voice data are trimmed by hamming window method and voice attribute part of the digital data is obtained. For obtaining attribute of voice data LPC (linear predictive coding and DFT (discrete fourier transform methods are used. Of those 28 coefficents, that is used for speaker recognition, 16 were obtained by the analysis of DFT and 12 were obtained by the analysis of LPC. The parameters that represent speaker voice, is used for training and test of ANN. Multilayer perceptron model is used as an architecture of ANN and backpropagation algorithm is used for training method. Voices of "a" is taken from 7 different person and their attributes are found. ANN is trained with these features to find the speaker who is the owner of the sample voice. And then using the test data that is not used for training part, recognition achievement of ANN is tested. As a result, good results were obtained with low failure rate.

  2. Application of artificial neural network in medical geochemistry.

    Science.gov (United States)

    Fajčíková, K; Stehlíková, B; Cvečková, V; Rapant, S

    2017-12-01

    For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of population (so-called health indicators) play a key role. Geochemical and health data are predominantly nonlinear, and the use of standard statistical methods can lead to wrong conclusions. For linking such data, we find appropriate the use method of artificial neural networks (ANNs) which enable to eliminate data inhomogeneity and also potential data errors. Through method of ANNs, we are able to determine the order of influence of chemical elements on health indicators as well as to define limit values for the influential elements at which the health status of population is the most favourable (i.e. the lowest mortality, the highest life expectancy). For determination of dependence between the groundwater contents of chemical elements and health indicators, we recommend to create 200 ANNs. In further calculations performed for identification of order of influence of chemical elements as well as definition of limit values, we propose to work with median or mean values from calculated 200 ANNs. The ANN represents an appropriate method to be used for environmental and health data analysis in medical geochemistry.

  3. Application of Artificial Neural Network to Predict the use of Runway at Juanda International Airport

    Science.gov (United States)

    Putra, J. C. P.; Safrilah

    2017-06-01

    Artificial neural network approaches are useful to solve many complicated problems. It solves a number of problems in various areas such as engineering, medicine, business, manufacturing, etc. This paper presents an application of artificial neural network to predict a runway capacity at Juanda International Airport. An artificial neural network model of backpropagation and multi-layer perceptron is adopted to this research to learning process of runway capacity at Juanda International Airport. The results indicate that the training data is successfully recognizing the certain pattern of runway use at Juanda International Airport. Whereas, testing data indicate vice versa. Finally, it can be concluded that the approach of uniformity data and network architecture is the critical part to determine the accuracy of prediction results.

  4. ER fluid applications to vibration control devices and an adaptive neural-net controller

    Science.gov (United States)

    Morishita, Shin; Ura, Tamaki

    1993-07-01

    Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.

  5. Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

    Science.gov (United States)

    Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

    2017-11-01

    In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

  6. Evolvable Block-Based Neural Network Design for Applications in Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Saumil G. Merchant

    2010-01-01

    Full Text Available Dedicated hardware implementations of artificial neural networks promise to provide faster, lower-power operation when compared to software implementations executing on microprocessors, but rarely do these implementations have the flexibility to adapt and train online under dynamic conditions. A typical design process for artificial neural networks involves offline training using software simulations and synthesis and hardware implementation of the obtained network offline. This paper presents a design of block-based neural networks (BbNNs on FPGAs capable of dynamic adaptation and online training. Specifically the network structure and the internal parameters, the two pieces of the multiparametric evolution of the BbNNs, can be adapted intrinsically, in-field under the control of the training algorithm. This ability enables deployment of the platform in dynamic environments, thereby significantly expanding the range of target applications, deployment lifetimes, and system reliability. The potential and functionality of the platform are demonstrated using several case studies.

  7. Application of neural models as controllers in mobile robot velocity control loop

    Science.gov (United States)

    Cerkala, Jakub; Jadlovska, Anna

    2017-01-01

    This paper presents the application of an inverse neural models used as controllers in comparison to classical PI controllers for velocity tracking control task used in two-wheel, differentially driven mobile robot. The PI controller synthesis is based on linear approximation of actuators with equivalent load. In order to obtain relevant datasets for training of feed-forward multi-layer perceptron based neural network used as neural model, the mathematical model of mobile robot, that combines its kinematic and dynamic properties such as chassis dimensions, center of gravity offset, friction and actuator parameters is used. Neural models are trained off-line to act as an inverse dynamics of DC motors with particular load using data collected in simulation experiment for motor input voltage step changes within bounded operating area. The performances of PI controllers versus inverse neural models in mobile robot internal velocity control loops are demonstrated and compared in simulation experiment of navigation control task for line segment motion in plane.

  8. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    user

    MultiCraft. International Journal of Engineering, Science and Technology. Vol. 2, No. 3, 2010, pp. 19-28. INTERNATIONAL. JOURNAL OF. ENGINEERING,. SCIENCE AND. TECHNOLOGY ... state estimation is investigated by testing its applicability on a IEEE 14 bus system. The proposed estimator is compared with.

  9. A Monte Carlo EM approach for partially observable diffusion processes: theory and applications to neural networks.

    Science.gov (United States)

    Movellan, Javier R; Mineiro, Paul; Williams, R J

    2002-07-01

    We present a Monte Carlo approach for training partially observable diffusion processes. We apply the approach to diffusion networks, a stochastic version of continuous recurrent neural networks. The approach is aimed at learning probability distributions of continuous paths, not just expected values. Interestingly, the relevant activation statistics used by the learning rule presented here are inner products in the Hilbert space of square integrable functions. These inner products can be computed using Hebbian operations and do not require backpropagation of error signals. Moreover, standard kernel methods could potentially be applied to compute such inner products. We propose that the main reason that recurrent neural networks have not worked well in engineering applications (e.g., speech recognition) is that they implicitly rely on a very simplistic likelihood model. The diffusion network approach proposed here is much richer and may open new avenues for applications of recurrent neural networks. We present some analysis and simulations to support this view. Very encouraging results were obtained on a visual speech recognition task in which neural networks outperformed hidden Markov models.

  10. Prosthetic stomatitis with removable dentures

    Directory of Open Access Journals (Sweden)

    Rozalieva Yu.Yu.

    2012-06-01

    Full Text Available The Research Objective: To study patients with prosthetic stomatitis, who use the removable laminar dentures. Materials: The consultations and treatment of 79 patients aged 47-65 years have been conducted. The patients have been divided into two clinical groups. The first clinical group (39 persons with the performance of immediate prosthet-ics; the second control clinical group (40 persons — the permanent dentures were produced without the preliminary instruction. Results: All the patients, having the laminar dentures without the preliminary use of immediate constructions of dentures, in spite of repeated correction of them, have had changes of dentures and transitory fold. Patients have been exposed to prosthetic stomatitis of different etiology (without trauma; the single-shot or multiple correction of dentures by the method of rebasing with using of cold cure plastics has been made. Conclusion: Structural and functional changes of dentition during the prosthetic stomatitis lead to disorders, associated by the mucositis. Use of the term of «prosthetic stomatitis» reflects etiological and pathogenetic component of changes in the denture-supporting tissues

  11. Image processing using pulse-coupled neural networks applications in Python

    CERN Document Server

    Lindblad, Thomas

    2013-01-01

    Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

  12. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  13. NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

    Directory of Open Access Journals (Sweden)

    José Alonso Borba

    2010-04-01

    Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.

  14. Prosthetic helping hand

    Science.gov (United States)

    Vest, Thomas W. (Inventor); Carden, James R. (Inventor); Norton, William E. (Inventor); Belcher, Jewell G. (Inventor)

    1992-01-01

    A prosthetic device for below-the-elbow amputees, having a C-shaped clamping mechanism for grasping cylindrical objects, is described. The clamping mechanism is pivotally mounted to a cuff that fits on the amputee's lower arm. The present invention is utilized by placing an arm that has been amputated below the elbow into the cuff. The clamping mechanism then serves as a hand whenever it becomes necessary for the amputee to grasp a cylindrical object such as a handle, a bar, a rod, etc. To grasp the cylindrical object, the object is jammed against the opening in the C-shaped spring, causing the spring to open, the object to pass to the center of the spring, and the spring to snap shut behind the object. Various sizes of clamping mechanisms can be provided and easily interchanged to accommodate a variety of diameters. With the extension that pivots and rotates, the clamping mechanism can be used in a variety of orientations. Thus, this invention provides the amputee with a clamping mechanism that can be used to perform a number of tasks.

  15. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    Science.gov (United States)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  16. Wavelet neural networks with applications in financial engineering, chaos, and classification

    CERN Document Server

    Alexandridis, Antonios K

    2014-01-01

    Through extensive examples and case studies, Wavelet Neural Networks provides a step-by-step introduction to modeling, training, and forecasting using wavelet networks. The acclaimed authors present a statistical model identification framework to successfully apply wavelet networks in various applications, specifically, providing the mathematical and statistical framework needed for model selection, variable selection, wavelet network construction, initialization, training, forecasting and prediction, confidence intervals, prediction intervals, and model adequacy testing. The text is ideal for

  17. Nano-biotechnology: carbon nanofibres as improved neural and orthopaedic implants

    Science.gov (United States)

    Webster, Thomas J.; Waid, Michael C.; McKenzie, Janice L.; Price, Rachel L.; Ejiofor, Jeremiah U.

    2004-01-01

    For the continuous monitoring, diagnosis, and treatment of neural tissue, implantable probes are required. However, sometimes such neural probes (usually composed of silicon) become encapsulated with non-conductive, undesirable glial scar tissue. Similarly for orthopaedic implants, biomaterials (usually titanium and/or titanium alloys) often become encapsulated with undesirable soft fibrous, not hard bony, tissue. Although possessing intriguing electrical and mechanical properties for neural and orthopaedic applications, carbon nanofibres/nanotubes have not been widely considered for these applications to date. The present work developed a carbon nanofibre reinforced polycarbonate urethane (PU) composite in an attempt to determine the possibility of using carbon nanofibres (CNs) as either neural or orthopaedic prosthetic devices. Electrical and mechanical characterization studies determined that such composites have properties suitable for neural and orthopaedic applications. More importantly, cell adhesion experiments revealed for the first time the promise these materials have to increase neural (nerve cell) and osteoblast (bone-forming cell) functions. In contrast, functions of cells that contribute to glial scar-tissue formation for neural prostheses (astrocytes) and fibrous-tissue encapsulation events for bone implants (fibroblasts) decreased on PU composites containing increasing amounts of CNs. In this manner, this study provided the first evidence of the future that CN formulations may have towards interacting with neural and bone cells which is important for the design of successful neural probes and orthopaedic implants, respectively.

  18. NONLINEAR SYSTEM MODELING USING SINGLE NEURON CASCADED NEURAL NETWORK FOR REAL-TIME APPLICATIONS

    Directory of Open Access Journals (Sweden)

    S. Himavathi

    2012-04-01

    Full Text Available Neural Networks (NN have proved its efficacy for nonlinear system modeling. NN based controllers and estimators for nonlinear systems provide promising alternatives to the conventional counterpart. However, NN models have to meet the stringent requirements on execution time for its effective use in real time applications. This requires the NN model to be structurally compact and computationally less complex. In this paper a parametric method of analysis is adopted to determine the compact and faster NN model among various neural network architectures. This work proves through analysis and examples that the Single Neuron Cascaded (SNC architecture is distinct in providing compact and simpler models requiring lower execution time. The unique structural growth of SNC architecture enables automation in design. The SNC Network is shown to combine the advantages of both single and multilayer neural network architectures. Extensive analysis on selected architectures and their models for four benchmark nonlinear theoretical plants and a practical application are tested. A performance comparison of the NN models is presented to demonstrate the superiority of the single neuron cascaded architecture for online real time applications.

  19. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  20. Applications of Mesenchymal Stem Cells and Neural Crest Cells in Craniofacial Skeletal Research

    Directory of Open Access Journals (Sweden)

    Satoru Morikawa

    2016-01-01

    Full Text Available Craniofacial skeletal tissues are composed of tooth and bone, together with nerves and blood vessels. This composite material is mainly derived from neural crest cells (NCCs. The neural crest is transient embryonic tissue present during neural tube formation whose cells have high potential for migration and differentiation. Thus, NCCs are promising candidates for craniofacial tissue regeneration; however, the clinical application of NCCs is hindered by their limited accessibility. In contrast, mesenchymal stem cells (MSCs are easily accessible in adults, have similar potential for self-renewal, and can differentiate into skeletal tissues, including bones and cartilage. Therefore, MSCs may represent good sources of stem cells for clinical use. MSCs are classically identified under adherent culture conditions, leading to contamination with other cell lineages. Previous studies have identified mouse- and human-specific MSC subsets using cell surface markers. Additionally, some studies have shown that a subset of MSCs is closely related to neural crest derivatives and endothelial cells. These MSCs may be promising candidates for regeneration of craniofacial tissues from the perspective of developmental fate. Here, we review the fundamental biology of MSCs in craniofacial research.

  1. An Examination of Application of Artificial Neural Network in Cognitive Radios

    Science.gov (United States)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  2. Neural Network Based Recognition of Signal Patterns in Application to Automatic Testing of Rails

    Directory of Open Access Journals (Sweden)

    Tomasz Ciszewski

    2006-01-01

    Full Text Available The paper describes the application of neural network for recognition of signal patterns in measuring data gathered by the railroad ultrasound testing car. Digital conversion of the measuring signal allows to store and process large quantities of data. The elaboration of smart, effective and automatic procedures recognizing the obtained patterns on the basisof measured signal amplitude has been presented. The test shows only two classes of pattern recognition. In authors’ opinion if we deliver big enough quantity of training data, presented method is applicable to a system that recognizes many classes.

  3. "Geo-statistics methods and neural networks in geophysical applications: A case study"

    Science.gov (United States)

    Rodriguez Sandoval, R.; Urrutia Fucugauchi, J.; Ramirez Cruz, L. C.

    2008-12-01

    The study is focus in the Ebano-Panuco basin of northeastern Mexico, which is being explored for hydrocarbon reservoirs. These reservoirs are in limestones and there is interest in determining porosity and permeability in the carbonate sequences. The porosity maps presented in this study are estimated from application of multiattribute and neural networks techniques, which combine geophysics logs and 3-D seismic data by means of statistical relationships. The multiattribute analysis is a process to predict a volume of any underground petrophysical measurement from well-log and seismic data. The data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs are neutron porosity logs. From the 3-D seismic volume a series of sample attributes is calculated. The objective of this study is to derive a set of attributes and the target log values. The selected set is determined by a process of forward stepwise regression. The analysis can be linear or nonlinear. In the linear mode the method consists of a series of weights derived by least-square minimization. In the nonlinear mode, a neural network is trained using the select attributes as inputs. In this case we used a probabilistic neural network PNN. The method is applied to a real data set from PEMEX. For better reservoir characterization the porosity distribution was estimated using both techniques. The case shown a continues improvement in the prediction of the porosity from the multiattribute to the neural network analysis. The improvement is in the training and the validation, which are important indicators of the reliability of the results. The neural network showed an improvement in resolution over the multiattribute analysis. The final maps provide more realistic results of the porosity distribution.

  4. Providing a sense of touch to prosthetic hands.

    Science.gov (United States)

    Nghiem, Bao Tram; Sando, Ian C; Gillespie, R Brent; McLaughlin, Bryan L; Gerling, Gregory J; Langhals, Nicholas B; Urbanchek, Melanie G; Cederna, Paul S

    2015-06-01

    Each year, approximately 185,000 Americans suffer the devastating loss of a limb. The effects of upper limb amputations are profound because a person's hands are tools for everyday functioning, expressive communication, and other uniquely human attributes. Despite the advancements in prosthetic technology, current upper limb prostheses are still limited in terms of complex motor control and sensory feedback. Sensory feedback is critical to restoring full functionality to amputated patients because it would relieve the cognitive burden of relying solely on visual input to monitor motor commands and provide tremendous psychological benefits. This article reviews the latest innovations in sensory feedback and argues in favor of peripheral nerve interfaces. First, the authors examine the structure of the peripheral nerve and its importance in the development of a sensory interface. Second, the authors discuss advancements in targeted muscle reinnervation and direct neural stimulation by means of intraneural electrodes. The authors then explore the future of prosthetic sensory feedback using innovative technologies for neural signaling, specifically, the sensory regenerative peripheral nerve interface and optogenetics. These breakthroughs pave the way for the development of a prosthetic limb with the ability to feel.

  5. Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm

    Directory of Open Access Journals (Sweden)

    Veronica Chan

    2017-03-01

    Full Text Available This paper presents the application of a neural network rule extraction algorithm, called the piece-wise linear artificial neural network or PWL-ANN algorithm, on a carbon capture process system dataset. The objective of the application is to enhance understanding of the intricate relationships among the key process parameters. The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN. The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach, in which accuracies of the generated predictive models are often not satisfactory, and the opaqueness of the ANN models. The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system. An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO2 production rate are the steam flow rate through reboiler, reboiler pressure, and the CO2 concentration in the flue gas.

  6. Study of functional viability of SU-8-based microneedles for neural applications

    Science.gov (United States)

    Fernández, Luis J.; Altuna, Ane; Tijero, Maria; Gabriel, Gemma; Villa, Rosa; Rodríguez, Manuel J.; Batlle, Montse; Vilares, Roman; Berganzo, Javier; Blanco, F. J.

    2009-02-01

    This paper presents the design, fabrication, packaging and first test results of SU-8-based microneedles for neural applications. By the use of photolithography, sputtering and bonding techniques, polymer needles with integrated microchannels and electrodes have been successfully fabricated. The use of photolithography for the patterning of the fluidic channel integrated in the needle allows the design of multiple outlet ports at the needle tip, minimizing the possibility of being blocked by the tissue. Furthermore, the flexibility of the polymer reduces the risk of fracture and tissue damage once the needle is inserted, while it is still rigid enough to allow a perfect insertion into the neural tissue. Fluidic and electric characterization of the microneedles has shown their viability for drug delivery and monitoring in neural applications. First drug delivery tests in ex vivo tissue demonstrated the functional viability of the needle to deliver drugs to precise points. Furthermore, in vivo experiments have demonstrated lower associated damages during insertion than those by stereotaxic standard needles.

  7. Application of neural network in market segmentation: A review on recent trends

    Directory of Open Access Journals (Sweden)

    Manojit Chattopadhyay

    2012-04-01

    Full Text Available Despite the significance of Artificial Neural Network (ANN algorithm to market segmentation, there is a need of a comprehensive literature review and a classification system for it towards identification of future trend of market segmentation research. The present work is the first identifiable academic literature review of the application of neural network based techniques to segmentation. Our study has provided an academic database of literature between the periods of 2000–2010 and proposed a classification scheme for the articles. One thousands (1000 articles have been identified, and around 100 relevant selected articles have been subsequently reviewed and classified based on the major focus of each paper. Findings of this study indicated that the research area of ANN based applications are receiving most research attention and self organizing map based applications are second in position to be used in segmentation. The commonly used models for market segmentation are data mining, intelligent system etc. Our analysis furnishes a roadmap to guide future research and aid knowledge accretion and establishment pertaining to the application of ANN based techniques in market segmentation. Thus the present work will significantly contribute to both the industry and academic research in business and marketing as a sustainable valuable knowledge source of market segmentation with the future trend of ANN application in segmentation.

  8. A COMPARATIVE STUDY ON THE EFFECTS OF THE POLYMER PROSTHETIC BASE PRESSURE OVER THE PROSTHETIC FIELD

    Directory of Open Access Journals (Sweden)

    A.T. Cigu

    2016-04-01

    Full Text Available Partially mobile polymeric prostheses constitute absolutely necessary therapeutical means in all forms of partial edentations. As known, polymeric partial prostheses constitute temporary solutions for the treatment of edentaton states. Nowadays, this treatment includes traditional acrylic prostheses, as well as elastic prostheses made of different material systems. Such a system is Valplast, which uses a polyamide for the realization of the prosthetic base. Both the rigid and the elastic materials are nowadays under debate, different – positive or negative – opinions being uttered in relation with their utilization. The scope of the present study is to support the intensive application of the elastic materials. Extremely important is the identification of the intrinsic qualities of the materials influencing the behaviour in the oral cavity, especially the effects of pressure upon the biological structures of the prosthetic field.

  9. Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

    Directory of Open Access Journals (Sweden)

    Chunqing Li

    2012-01-01

    Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

  10. Parzen neural networks: Fundamentals, properties, and an application to forensic anthropology.

    Science.gov (United States)

    Trentin, Edmondo; Lusnig, Luca; Cavalli, Fabio

    2017-10-18

    A novel, unsupervised nonparametric model of multivariate probability density functions (pdf) is introduced, namely the Parzen neural network (PNN). The PNN is intended to overcome the major limitations of traditional (either statistical or neural) pdf estimation techniques. Besides being profitably simple, the PNN turns out to have nice properties in terms of unbiased modeling capability, asymptotic convergence, and efficiency at test time. Several matters pertaining the practical application of the PNN are faced in the paper, too. Experiments are reported, involving (i) synthetic datasets, and (ii) a challenging sex determination task from 1400 scout-view CT-scan images of human crania. Incidentally, the empirical evidence entails also some conclusions of high anthropological relevance. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Development of a 3D-Printed Robotic Prosthetic Arm

    Energy Technology Data Exchange (ETDEWEB)

    Gomez Martinez, M.; Garcia-Miquel, A.; Vidal Martinez, N.

    2016-07-01

    Current prostheses are not affordable to the general public. 3D printing technology may allow low-cost production of such devices, making them more readily accessible to people in need. This contribution presents the set-up and the considerations that have to be taken into account to develop a functional artificial upper limb prototype. The robotic prosthetic arm reported herein was produced entirely using 3D printing technology to demonstrate its feasibility on a limited budget. The project was developed to integrate two different functional modes: a prosthetic application and a remote application. The prosthetic application is intended to emulate existing prosthetic devices using myoelectric sensors. The remote application is conceived as a tool for prevention, by providing the general public with a device that could carry out activities that entail a risk of severe physical injury. This is achieved using a hand-tracking system that allows the robotic arm to copy the user’s movements remotely and in real time. The outcome of the validation tests has been considerably successful for both applications and the total costs are on target. (Author)

  12. Candida infection of a prosthetic shoulder joint

    Energy Technology Data Exchange (ETDEWEB)

    Lichtman, E.A.

    1983-09-01

    A heroin addict developed a Candida parapsilosis infection in a prosthetic shoulder joint. Radiographs showed loose fragments of cement with prosthetic loosening. The patient was treated with removal of the prosthesis and intravenous amphotericin B followed by oral ketoconazole.

  13. Aerogel Use as a Skin Protective Liner In Space Suits and Prosthetic Limbs Project

    Science.gov (United States)

    Roberson, Luke Bennett

    2014-01-01

    Existing materials for prosthetic liners tend to be thick and airtight, causing perspiration to accumulate inside the liner and potentially causing infection and injury. The purpose of this project was to examine the suitability of aerogel for prosthetic liner applications for use in space suits and orthopedics. Three tests were performed on several types of aerogel to assess the properties of each material, and our initial findings demonstrated that these materrials would be excellent candidates for liner applications for prosthetics and space suits. The project is currently on hold until additional funding is obtained for application testing at the VH Hospitals in Tampa

  14. Rothia prosthetic knee joint infection.

    Science.gov (United States)

    Trivedi, Manish N; Malhotra, Prashant

    2015-08-01

    Rothia species - Gram-positive pleomorphic bacteria that are part of the normal oral and respiratory flora - are commonly associated with dental cavities and periodontal disease although systemic infections have been described. We describe a 53-year-old female with rheumatoid arthritis complicated by prosthetic knee joint infection due to Rothia species, which was successfully treated by surgical removal of prosthesis and prolonged antimicrobial therapy. The issue of antibiotic prophylaxis before dental procedures among patients with prosthetic joint replacements is discussed. Copyright © 2012. Published by Elsevier B.V.

  15. Control of Prosthetic Hands via the Peripheral Nervous System.

    Science.gov (United States)

    Ciancio, Anna Lisa; Cordella, Francesca; Barone, Roberto; Romeo, Rocco Antonio; Bellingegni, Alberto Dellacasa; Sacchetti, Rinaldo; Davalli, Angelo; Di Pino, Giovanni; Ranieri, Federico; Di Lazzaro, Vincenzo; Guglielmelli, Eugenio; Zollo, Loredana

    2016-01-01

    This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

  16. Control of prosthetic hands via the peripheral nervous system

    Directory of Open Access Journals (Sweden)

    Anna Lisa eCiancio

    2016-04-01

    Full Text Available This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e. PNS, and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

  17. Control of Prosthetic Hands via the Peripheral Nervous System

    Science.gov (United States)

    Ciancio, Anna Lisa; Cordella, Francesca; Barone, Roberto; Romeo, Rocco Antonio; Bellingegni, Alberto Dellacasa; Sacchetti, Rinaldo; Davalli, Angelo; Di Pino, Giovanni; Ranieri, Federico; Di Lazzaro, Vincenzo; Guglielmelli, Eugenio; Zollo, Loredana

    2016-01-01

    This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control. PMID:27092041

  18. Performance characteristics of anthropomorphic prosthetic hands.

    Science.gov (United States)

    Belter, Joseph T; Dollar, Aaron M

    2011-01-01

    In this paper we set forth a review of performance characteristics for both common commercial prosthetics as well as anthropomorphic research devices. Based on these specifications as well as surveyed results from prosthetic users, ranges of hand attributes are evaluated and discussed. End user information is used to describe the performance requirements for prosthetic hands for clinical use. © 2011 IEEE

  19. Neural networks for learning and prediction with applications to remote sensing and speech perception

    Science.gov (United States)

    Gjaja, Marin N.

    1997-11-01

    Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An

  20. Artificial Neural Networks for Processing Graphs with Application to Image Understanding: A Survey

    Science.gov (United States)

    Bianchini, Monica; Scarselli, Franco

    In graphical pattern recognition, each data is represented as an arrangement of elements, that encodes both the properties of each element and the relations among them. Hence, patterns are modelled as labelled graphs where, in general, labels can be attached to both nodes and edges. Artificial neural networks able to process graphs are a powerful tool for addressing a great variety of real-world problems, where the information is naturally organized in entities and relationships among entities and, in fact, they have been widely used in computer vision, f.i. in logo recognition, in similarity retrieval, and for object detection. In this chapter, we propose a survey of neural network models able to process structured information, with a particular focus on those architectures tailored to address image understanding applications. Starting from the original recursive model (RNNs), we subsequently present different ways to represent images - by trees, forests of trees, multiresolution trees, directed acyclic graphs with labelled edges, general graphs - and, correspondingly, neural network architectures appropriate to process such structures.

  1. Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

    Science.gov (United States)

    Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

    2017-11-01

    In Hezaveh et al. we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational-lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data, as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single variational parameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that the application of approximate Bayesian neural networks to astrophysical modeling problems can be a fast alternative to Monte Carlo Markov Chains, allowing orders of magnitude improvement in speed.

  2. Gait Phases Recognition from Accelerations and Ground Reaction Forces: Application of Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Rafajlović

    2009-06-01

    Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.

  3. Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

    Science.gov (United States)

    Bildirici, Melike; Ersin, Özgür

    2014-01-01

    The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

  4. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns

    Directory of Open Access Journals (Sweden)

    Melike Bildirici

    2014-01-01

    Full Text Available The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100. Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications.

  5. Porosity Estimation By Artificial Neural Networks Inversion . Application to Algerian South Field

    Science.gov (United States)

    Eladj, Said; Aliouane, Leila; Ouadfeul, Sid-Ali

    2017-04-01

    One of the main geophysicist's current challenge is the discovery and the study of stratigraphic traps, this last is a difficult task and requires a very fine analysis of the seismic data. The seismic data inversion allows obtaining lithological and stratigraphic information for the reservoir characterization . However, when solving the inverse problem we encounter difficult problems such as: Non-existence and non-uniqueness of the solution add to this the instability of the processing algorithm. Therefore, uncertainties in the data and the non-linearity of the relationship between the data and the parameters must be taken seriously. In this case, the artificial intelligence techniques such as Artificial Neural Networks(ANN) is used to resolve this ambiguity, this can be done by integrating different physical properties data which requires a supervised learning methods. In this work, we invert the acoustic impedance 3D seismic cube using the colored inversion method, then, the introduction of the acoustic impedance volume resulting from the first step as an input of based model inversion method allows to calculate the Porosity volume using the Multilayer Perceptron Artificial Neural Network. Application to an Algerian South hydrocarbon field clearly demonstrate the power of the proposed processing technique to predict the porosity for seismic data, obtained results can be used for reserves estimation, permeability prediction, recovery factor and reservoir monitoring. Keywords: Artificial Neural Networks, inversion, non-uniqueness , nonlinear, 3D porosity volume, reservoir characterization .

  6. A mechatronics platform to study prosthetic hand control using EMG signals.

    Science.gov (United States)

    Geethanjali, P

    2016-09-01

    In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time

  7. Prosthetic Hand Lifts Heavy Loads

    Science.gov (United States)

    Carden, James R.; Norton, William; Belcher, Jewell G.; Vest, Thomas W.

    1991-01-01

    Prosthetic hand designed to enable amputee to lift diverse heavy objects like rocks and logs. Has simple serrated end effector with no moving parts. Prosthesis held on forearm by system of flexible straps. Features include ruggedness, simplicity, and relatively low cost.

  8. Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation

    Science.gov (United States)

    Banish, Michele R.; Ranganath, Heggere S.; Karpinsky, John R.; Clark, Rodney L.; Germany, Glynn A.; Richards, Philip G.

    1999-03-01

    Pulse Coupled Neural Networks have been extended and modified to suit image segmentation applications. Previous research demonstrated the ability of a PCNN to ignore noisy variations in intensity and small spatial discontinuities in images that prove beneficial to image segmentation and image smoothing. This paper describes four research and development projects that relate to PCNN segmentation - three different signal processing applications and a CMOS integrated circuit implementation. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT images of the heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global aurora images obtained from UV Imager. The paper also describes the hardware implementation of PCNN algorithm as an electro-optical chip.

  9. Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

    Directory of Open Access Journals (Sweden)

    Mohamed A. Shahin

    2009-01-01

    Full Text Available Artificial neural networks (ANNs are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classical mathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.

  10. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications Including Developmental Dyslexia.

    Science.gov (United States)

    Yuskaitis, Christopher J; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y; Pearl, Phillip L

    2015-08-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensorimotor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing and behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia.

  11. Neural Mechanisms Underlying Musical Pitch Perception and Clinical Applications including Developmental Dyselxia

    Science.gov (United States)

    Yuskaitis, Christopher J.; Parviz, Mahsa; Loui, Psyche; Wan, Catherine Y.; Pearl, Phillip L.

    2017-01-01

    Music production and perception invoke a complex set of cognitive functions that rely on the integration of sensory-motor, cognitive, and emotional pathways. Pitch is a fundamental perceptual attribute of sound and a building block for both music and speech. Although the cerebral processing of pitch is not completely understood, recent advances in imaging and electrophysiology have provided insight into the functional and anatomical pathways of pitch processing. This review examines the current understanding of pitch processing, behavioral and neural variations that give rise to difficulties in pitch processing, and potential applications of music education for language processing disorders such as dyslexia. PMID:26092314

  12. Radial basis function neural networks with sequential learning MRAN and its applications

    CERN Document Server

    Sundararajan, N; Wei Lu Ying

    1999-01-01

    This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t

  13. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  14. Fiber-array based optogenetic prosthetic system for stimulation therapy

    Science.gov (United States)

    Gu, Ling; Cote, Chris; Tejeda, Hector; Mohanty, Samarendra

    2012-02-01

    Recent advent of optogenetics has enabled activation of genetically-targeted neuronal cells using low intensity blue light with high temporal precision. Since blue light is attenuated rapidly due to scattering and absorption in neural tissue, optogenetic treatment of neurological disorders may require stimulation of specific cell types in multiple regions of the brain. Further, restoration of certain neural functions (vision, and auditory etc) requires accurate spatio-temporal stimulation patterns rather than just precise temporal stimulation. In order to activate multiple regions of the central nervous system in 3D, here, we report development of an optogenetic prosthetic comprising of array of fibers coupled to independently-controllable LEDs. This design avoids direct contact of LEDs with the brain tissue and thus does not require electrical and heat isolation, which can non-specifically stimulate and damage the local brain regions. The intensity, frequency, and duty cycle of light pulses from each fiber in the array was controlled independently using an inhouse developed LabView based program interfaced with a microcontroller driving the individual LEDs. While the temporal profile of the light pulses was controlled by varying the current driving the LED, the beam profile emanating from each fiber tip could be sculpted by microfabrication of the fiber tip. The fiber array was used to stimulate neurons, expressing channelrhodopsin-2, in different locations within the brain or retina. Control of neural activity in the mice cortex, using the fiber-array based prosthetic, is evaluated from recordings made with multi-electrode array (MEA). We also report construction of a μLED array based prosthetic for spatio-temporal stimulation of cortex.

  15. Identification and quantification of prosthetic mitral regurgitation by flow convergence method using transthoracic approach

    Directory of Open Access Journals (Sweden)

    Roux Emmanuel

    2009-02-01

    Full Text Available Abstract The present case report illustrates the clinical applicability of the proximal isovelocity surface area (PISA method in identifying, locating and assessing paravalvular prosthetic mitral regurgitation by transthoracic echocardiography.

  16. Identification and quantification of prosthetic mitral regurgitation by flow convergence method using transthoracic approach

    OpenAIRE

    Roux Emmanuel; Leonnet Caroline; Arques Stephane; Avierinos Jean-François

    2009-01-01

    Abstract The present case report illustrates the clinical applicability of the proximal isovelocity surface area (PISA) method in identifying, locating and assessing paravalvular prosthetic mitral regurgitation by transthoracic echocardiography.

  17. Neural networks, fuzzy logic and genetic algorithms: applications and possibilities in finance and accounting/ Redes neurais, logica nebulosa e algoritmos geneticos: aplicacoes e possibilidades em financas e contabilidade

    National Research Council Canada - National Science Library

    Wuerges, Artur Filipe Ewald; Borba, Jose Alonso

    2010-01-01

    .... This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to...

  18. Bayesian neural networks for bivariate binary data: an application to prostate cancer study.

    Science.gov (United States)

    Chakraborty, Sounak; Ghosh, Malay; Maiti, Tapabrata; Tewari, Ashutosh

    2005-12-15

    Prostate cancer is one of the most common cancers in American men. The cancer could either be locally confined, or it could spread outside the organ. When locally confined, there are several options for treating and curing this disease. Otherwise, surgery is the only option, and in extreme cases of outside spread, it could very easily recur within a short time even after surgery and subsequent radiation therapy. Hence, it is important to know, based on pre-surgery biopsy results how likely the cancer is organ-confined or not. The paper considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ confined prostate cancer. In particular, we find such probabilities for margin positivity (MP) and seminal vesicle (SV) positivity jointly. The available training set consists of bivariate binary outcomes indicating the presence or absence of the two. In addition, we have certain covariates such as prostate specific antigen (PSA), gleason score and the indicator for the cancer to be unilateral or bilateral (i.e. spread on one or both sides) in one data set and gene expression microarrays in another data set. We take a hierarchical Bayesian neural network approach to find the posterior prediction probabilities for a test and validation set, and compare these with the actual outcomes for the first data set. In case of the microarray data we use leave one out cross-validation to access the accuracy of our method. We also demonstrate the superiority of our method to the other competing methods through a simulation study. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, our Bayesian bivariate neural network procedure is shown to be superior to the classical neural network, Radford Neal's Bayesian neural network as well as bivariate logistic models to predict jointly the MP and SV in a patient in both the

  19. Human-Derived Neurons and Neural Progenitor Cells in High Content Imaging Applications.

    Science.gov (United States)

    Harrill, Joshua A

    2018-01-01

    Due to advances in the fields of stem cell biology and cellular engineering, a variety of commercially available human-derived neurons and neural progenitor cells (NPCs) are now available for use in research applications, including small molecule efficacy or toxicity screening. The use of human-derived neural cells is anticipated to address some of the uncertainties associated with the use of nonhuman culture models or transformed cell lines derived from human tissues. Many of the human-derived neurons and NPCs currently available from commercial sources recapitulate critical process of nervous system development including NPC proliferation, neurite outgrowth, synaptogenesis, and calcium signaling, each of which can be evaluated using high content image analysis (HCA). Human-derived neurons and NPCs are also amenable to culture in multiwell plate formats and thus may be adapted for use in HCA-based screening applications. This article reviews various types of HCA-based assays that have been used in conjunction with human-derived neurons and NPC cultures. This article also highlights instances where lower throughput analysis of neurodevelopmental processes has been performed and which demonstrate a potential for adaptation to higher-throughout imaging methods. Finally, a generic protocol for evaluating neurite outgrowth in human-derived neurons using a combination of immunocytochemistry and HCA is presented. The information provided in this article is intended to serve as a resource for cell model and assay selection for those interested in evaluating neurodevelopmental processes in human-derived cells.

  20. Neural prostheses in clinical applications--trends from precision mechanics towards biomedical microsystems in neurological rehabilitation.

    Science.gov (United States)

    Stieglitz, T; Schuettler, M; Koch, K P

    2004-04-01

    Neural prostheses partially restore body functions by technical nerve excitation after trauma or neurological diseases. External devices and implants have been developed since the early 1960s for many applications. Several systems have reached nowadays clinical practice: Cochlea implants help the deaf to hear, micturition is induced by bladder stimulators in paralyzed persons and deep brain stimulation helps patients with Parkinson's disease to participate in daily life again. So far, clinical neural prostheses are fabricated with means of precision mechanics. Since microsystem technology opens the opportunity to design and develop complex systems with a high number of electrodes to interface with the nervous systems, the opportunity for selective stimulation and complex implant scenarios seems to be feasible in the near future. The potentials and limitations with regard to biomedical microdevices are introduced and discussed in this paper. Target specifications are derived from existing implants and are discussed on selected applications that has been investigated in experimental research: a micromachined implant to interface a nerve stump with a sieve electrode, cuff electrodes with integrated electronics, and an epiretinal vision prosthesis.

  1. A hybrid neural network structure for application to nondestructive TRU waste assay

    Energy Technology Data Exchange (ETDEWEB)

    Becker, G. [Idaho National Engineering Lab., Idaho Falls, ID (United States)

    1995-12-31

    The determination of transuranic (TRU) and associated radioactive material quantities entrained in waste forms is a necessary component. of waste characterization. Measurement performance requirements are specified in the National TRU Waste Characterization Program quality assurance plan for which compliance must be demonstrated prior to the transportation and disposition of wastes. With respect to this criterion, the existing TRU nondestructive waste assay (NDA) capability is inadequate for a significant fraction of the US Department of Energy (DOE) complex waste inventory. This is a result of the general application of safeguard-type measurement and calibration schemes to waste form configurations. Incompatibilities between such measurement methods and actual waste form configurations complicate regulation compliance demonstration processes and illustrate the need for an alternate measurement interpretation paradigm. Hence, it appears necessary to supplement or perhaps restructure the perceived solution and approach to the waste NDA problem. The first step is to understand the magnitude of the waste matrix/source attribute space associated with those waste form configurations in inventory and how this creates complexities and unknowns with respect to existing NDA methods. Once defined and/or bounded, a conceptual method must be developed that specifies the necessary tools and the framework in which the tools are used. A promising framework is a hybridized neural network structure. Discussed are some typical complications associated with conventional waste NDA techniques and how improvements can be obtained through the application of neural networks.

  2. Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems

    Directory of Open Access Journals (Sweden)

    D. T. Yakupov

    2017-01-01

    Full Text Available The purpose of research – to identify the prospects for the use of neural network approach in relation to the tasks of economic forecasting of logistics performance, in particular of volume freight traffic in the transport system promiscuous regional freight traffic, as well as to substantiate the effectiveness of the use of artificial neural networks (ANN, as compared with the efficiency of traditional extrapolative methods of forecasting. The authors consider the possibility of forecasting to use ANN for these economic indicators not as an alternative to the traditional methods of statistical forecasting, but as one of the available simple means for solving complex problems.Materials and methods. When predicting the ANN, three methods of learning were used: 1 the Levenberg-Marquardt algorithm-network training stops when the generalization ceases to improve, which is shown by the increase in the mean square error of the output value; 2 Bayes regularization method - network training is stopped in accordance with the minimization of adaptive weights; 3 the method of scaled conjugate gradients, which is used to find the local extremum of a function on the basis of information about its values and gradient. The Neural Network Toolbox package is used for forecasting. The neural network model consists of a hidden layer of neurons with a sigmoidal activation function and an output neuron with a linear activation function, the input values of the dynamic time series, and the predicted value is removed from the output. For a more objective assessment of the prospects of the ANN application, the results of the forecast are presented in comparison with the results obtained in predicting the method of exponential smoothing.Results. When predicting the volumes of freight transportation by rail, satisfactory indicators of the verification of forecasting by both the method of exponential smoothing and ANN had been obtained, although the neural network

  3. Engineering applications of fpgas chaotic systems, artificial neural networks, random number generators, and secure communication systems

    CERN Document Server

    Tlelo-Cuautle, Esteban; de la Fraga, Luis Gerardo

    2016-01-01

    This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. Readers will b...

  4. Application of Electrospun Nanofibrous PHBV Scaffold in Neural Graft and Regeneration: A Mini-Review

    Directory of Open Access Journals (Sweden)

    Ali Gheibi

    2016-10-01

    Full Text Available Among the synthetic polymers, poly (3-hydroxybutyrate-co-3-hydroxyvalerate (PHBV microbial polyester is one of the biocompatible and biodegradable copolymers in the nanomedicine scope. PHBV has key points and suitable properties to support cellular adhesion, proliferation and differentiation of nanofibers. Nanofibers are noticeably employed in order to enhance the performance of biomaterials, and could be effectively considered in this scope. Electrospinning is one of the well-known and practical methods that extremely employed in the construction of nanofibrous scaffolds for biomedical application and recently PHBV has exploited in nerve graft and regenerative medicine. PHBV composites nanofibrous scaffolds are able to be applied as promising materials in many fields, such as; wound healing and dressing, tissue engineering, targeted drug delivery systems, functional carries, biosensors or nano-biosensors and so on. In this mini-review, we attempt to provide a more detailed overview of the recent advances of PHBV electrospun nanofibers application in neural graft and regeneration.

  5. Prosthetics & Orthotics Manufacturing Initiative (POMI)

    Science.gov (United States)

    2012-12-21

    have promise in allowing sockets to be adjusted after manufacture. The most likely configuration involves placing shape-memory foams , which are...demonstrated that the liquid carbon dioxide-based system could provide cooling through the thermal resistances of the viscoelastic liner now commonly worn...When a patient’s residual limb is covered by the prosthetic system, including a viscoelastic liner, the limb is robbed of all natural mechanisms

  6. Bar-holding prosthetic limb

    Science.gov (United States)

    Vest, Thomas W. (Inventor); Norton, William E. (Inventor); Belcher, Jewell G. (Inventor); Carden, James R. (Inventor)

    1992-01-01

    A prosthetic device for below-the-elbow amputees is disclosed. The device has a removable effector, which is attached to the end of an arm cuff. The effector is comprised of a pair of C-shaped members that are oriented so as to face each other. Working in concert, the C-shaped members are able to hold a bar such as a chainsaw handle. A flat spring is fitted around the C-shaped members to hold them together.

  7. Refining mass formulas for astrophysical applications: A Bayesian neural network approach

    Science.gov (United States)

    Utama, R.; Piekarewicz, J.

    2017-10-01

    Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

  8. An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Zhang

    2011-12-01

    Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

  9. Application of Levenberg-Marquardt Optimization Algorithm Based Multilayer Neural Networks for Hydrological Time Series Modeling

    Directory of Open Access Journals (Sweden)

    Umut Okkan

    2011-07-01

    Full Text Available Recently, Artificial Neural Networks (ANN, which is mathematical modelingtools inspired by the properties of the biological neural system, has been typically used inthe studies of hydrological time series modeling. These modeling studies generally includethe standart feed forward backpropagation (FFBP algorithms such as gradient-descent,gradient-descent with momentum rate and, conjugate gradient etc. As the standart FFBPalgorithms have some disadvantages relating to the time requirement and slowconvergency in training, Newton and Levenberg-Marquardt algorithms, which arealternative approaches to standart FFBP algorithms, were improved and also used in theapplications. In this study, an application of Levenberg-Marquardt algorithm based ANN(LM-ANN for the modeling of monthly inflows of Demirkopru Dam, which is located inthe Gediz basin, was presented. The LM-ANN results were also compared with gradientdescentwith momentum rate algorithm based FFBP model (GDM-ANN. When thestatistics of the long-term and also seasonal-term outputs are compared, it can be seen thatthe LM-ANN model that has been developed, is more sensitive for prediction of theinflows. In addition, LM-ANN approach can be used for modeling of other hydrologicalcomponents in terms of a rapid assessment and its robustness.

  10. An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

    Directory of Open Access Journals (Sweden)

    Alex Alexandridis

    2018-01-01

    Full Text Available This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.

  11. A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

    Directory of Open Access Journals (Sweden)

    Xiaofang Hu

    2012-01-01

    Full Text Available Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1 nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2 it can separate stored patterns from superimposed input; (3 it can deal with one-to-many associative memory; (4 it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme.

  12. Efficient Simulation of Wing Modal Response: Application of 2nd Order Shape Sensitivities and Neural Networks

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    2000-01-01

    At the preliminary design stage of a wing structure, an efficient simulation, one needing little computation but yielding adequately accurate results for various response quantities, is essential in the search of optimal design in a vast design space. In the present paper, methods of using sensitivities up to 2nd order, and direct application of neural networks are explored. The example problem is how to decide the natural frequencies of a wing given the shape variables of the structure. It is shown that when sensitivities cannot be obtained analytically, the finite difference approach is usually more reliable than a semi-analytical approach provided an appropriate step size is used. The use of second order sensitivities is proved of being able to yield much better results than the case where only the first order sensitivities are used. When neural networks are trained to relate the wing natural frequencies to the shape variables, a negligible computation effort is needed to accurately determine the natural frequencies of a new design.

  13. Artificial neural networks: Principle and application to model based control of drying systems -- A review

    Energy Technology Data Exchange (ETDEWEB)

    Thyagarajan, T.; Ponnavaikko, M. [Crescent Engineering Coll., Madras (India); Shanmugam, J. [Madras Inst. of Tech. (India); Panda, R.C.; Rao, P.G. [Central Leather Research Inst., Madras (India)

    1998-07-01

    This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with up-to-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN. 118 refs.

  14. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    Science.gov (United States)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  15. Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

    Science.gov (United States)

    Kapania, Rakesh K.; Liu, Youhua

    1998-01-01

    The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

  16. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors

    Directory of Open Access Journals (Sweden)

    N. Sriraam

    2011-01-01

    Full Text Available A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  17. Quality-on-Demand Compression of EEG Signals for Telemedicine Applications Using Neural Network Predictors.

    Science.gov (United States)

    Sriraam, N

    2011-01-01

    A telemedicine system using communication and information technology to deliver medical signals such as ECG, EEG for long distance medical services has become reality. In either the urgent treatment or ordinary healthcare, it is necessary to compress these signals for the efficient use of bandwidth. This paper discusses a quality on demand compression of EEG signals using neural network predictors for telemedicine applications. The objective is to obtain a greater compression gains at a low bit rate while preserving the clinical information content. A two-stage compression scheme with a predictor and an entropy encoder is used. The residue signals obtained after prediction is first thresholded using various levels of thresholds and are further quantized and then encoded using an arithmetic encoder. Three neural network models, single-layer and multi-layer perceptrons and Elman network are used and the results are compared with linear predictors such as FIR filters and AR modeling. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as PRD, SNR, cross correlation and power spectral density. It is found from the results that the quality of the reconstructed signal is preserved at a low PRD thereby yielding better compression results compared to results obtained using lossless scheme.

  18. Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

    Science.gov (United States)

    Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

    2016-03-01

    Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.

  19. Broadband Prosthetic Interfaces: Combining Nerve Transfers and Implantable Multichannel EMG Technology to Decode Spinal Motor Neuron Activity.

    Science.gov (United States)

    Bergmeister, Konstantin D; Vujaklija, Ivan; Muceli, Silvia; Sturma, Agnes; Hruby, Laura A; Prahm, Cosima; Riedl, Otto; Salminger, Stefan; Manzano-Szalai, Krisztina; Aman, Martin; Russold, Michael-Friedrich; Hofer, Christian; Principe, Jose; Farina, Dario; Aszmann, Oskar C

    2017-01-01

    Modern robotic hands/upper limbs may replace multiple degrees of freedom of extremity function. However, their intuitive use requires a high number of control signals, which current man-machine interfaces do not provide. Here, we discuss a broadband control interface that combines targeted muscle reinnervation, implantable multichannel electromyographic sensors, and advanced decoding to address the increasing capabilities of modern robotic limbs. With targeted muscle reinnervation, nerves that have lost their targets due to an amputation are surgically transferred to residual stump muscles to increase the number of intuitive prosthetic control signals. This surgery re-establishes a nerve-muscle connection that is used for sensing nerve activity with myoelectric interfaces. Moreover, the nerve transfer determines neurophysiological effects, such as muscular hyper-reinnervation and cortical reafferentation that can be exploited by the myoelectric interface. Modern implantable multichannel EMG sensors provide signals from which it is possible to disentangle the behavior of single motor neurons. Recent studies have shown that the neural drive to muscles can be decoded from these signals and thereby the user's intention can be reliably estimated. By combining these concepts in chronic implants and embedded electronics, we believe that it is in principle possible to establish a broadband man-machine interface, with specific applications in prosthesis control. This perspective illustrates this concept, based on combining advanced surgical techniques with recording hardware and processing algorithms. Here we describe the scientific evidence for this concept, current state of investigations, challenges, and alternative approaches to improve current prosthetic interfaces.

  20. Broadband Prosthetic Interfaces: Combining Nerve Transfers and Implantable Multichannel EMG Technology to Decode Spinal Motor Neuron Activity

    Directory of Open Access Journals (Sweden)

    Konstantin D. Bergmeister

    2017-07-01

    Full Text Available Modern robotic hands/upper limbs may replace multiple degrees of freedom of extremity function. However, their intuitive use requires a high number of control signals, which current man-machine interfaces do not provide. Here, we discuss a broadband control interface that combines targeted muscle reinnervation, implantable multichannel electromyographic sensors, and advanced decoding to address the increasing capabilities of modern robotic limbs. With targeted muscle reinnervation, nerves that have lost their targets due to an amputation are surgically transferred to residual stump muscles to increase the number of intuitive prosthetic control signals. This surgery re-establishes a nerve-muscle connection that is used for sensing nerve activity with myoelectric interfaces. Moreover, the nerve transfer determines neurophysiological effects, such as muscular hyper-reinnervation and cortical reafferentation that can be exploited by the myoelectric interface. Modern implantable multichannel EMG sensors provide signals from which it is possible to disentangle the behavior of single motor neurons. Recent studies have shown that the neural drive to muscles can be decoded from these signals and thereby the user's intention can be reliably estimated. By combining these concepts in chronic implants and embedded electronics, we believe that it is in principle possible to establish a broadband man-machine interface, with specific applications in prosthesis control. This perspective illustrates this concept, based on combining advanced surgical techniques with recording hardware and processing algorithms. Here we describe the scientific evidence for this concept, current state of investigations, challenges, and alternative approaches to improve current prosthetic interfaces.

  1. Computer Aided Facial Prosthetics Manufacturing System

    Directory of Open Access Journals (Sweden)

    Peng H.K.

    2016-01-01

    Full Text Available Facial deformities can impose burden to the patient. There are many solutions for facial deformities such as plastic surgery and facial prosthetics. However, current fabrication method of facial prosthetics is high-cost and time consuming. This study aimed to identify a new method to construct a customized facial prosthetic. A 3D scanner, computer software and 3D printer were used in this study. Results showed that the new developed method can be used to produce a customized facial prosthetics. The advantages of the developed method over the conventional process are low cost, reduce waste of material and pollution in order to meet the green concept.

  2. Circuit For Control Of Electromechanical Prosthetic Hand

    Science.gov (United States)

    Bozeman, Richard J., Jr.

    1995-01-01

    Proposed circuit for control of electromechanical prosthetic hand derives electrical control signals from shoulder movements. Updated, electronic version of prosthesis, that includes two hooklike fingers actuated via cables from shoulder harness. Circuit built around favored shoulder harness, provides more dexterous movement, without incurring complexity of computer-controlled "bionic" or hydraulically actuated devices. Additional harness and potentiometer connected to similar control circuit mounted on other shoulder. Used to control stepping motor rotating hand about prosthetic wrist to one of number of angles consistent with number of digital outputs. Finger-control signals developed by circuit connected to first shoulder harness transmitted to prosthetic hand via sliprings at prosthetic wrist joint.

  3. Neural Network for Principal Component Analysis with Applications in Image Compression

    Directory of Open Access Journals (Sweden)

    Luminita State

    2007-04-01

    Full Text Available Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.

  4. Application of Artificial Neural Network into the Water Level Modeling and Forecast

    Directory of Open Access Journals (Sweden)

    Marzenna Sztobryn

    2013-06-01

    Full Text Available The dangerous sea and river water level increase does not only destroy the human lives, but also generate the severe flooding in coastal areas. The rapidly changes in the direction and velocity of wind and associated with them sea level changes could be the severe threat for navigation, especially on the fairways of small fishery harbors located in the river mouth. There is the area of activity of two external forcing: storm surges and flood wave. The aim of the work was the description of an application of Artificial Neural Network (ANN methodology into the water level forecast in the case study field in Swibno harbor located is located at 938.7 km of the Wisla River and at a distance of about 3 km up the mouth (Gulf of Gdansk - Baltic Sea.

  5. Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding.

    Science.gov (United States)

    Pérez-Rodríguez, P; Gianola, D; Weigel, K A; Rosa, G J M; Crossa, J

    2013-08-01

    In recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees, or both. These models include, among others, the Bayesian regularized neural networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R package described here (brnn) implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.

  6. Neural control of fast nonlinear systems--application to a turbocharged SI engine with VCT.

    Science.gov (United States)

    Colin, Guillaume; Chamaillard, Yann; Bloch, Gérard; Corde, Gilles

    2007-07-01

    Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

  7. Synchronization of Switched Interval Networks and Applications to Chaotic Neural Networks

    Directory of Open Access Journals (Sweden)

    Jinde Cao

    2013-01-01

    Full Text Available This paper investigates synchronization problem of switched delay networks with interval parameters uncertainty, based on the theories of the switched systems and drive-response technique, a mathematical model of the switched interval drive-response error system is established. Without constructing Lyapunov-Krasovskii functions, introducing matrix measure method for the first time to switched time-varying delay networks, combining Halanay inequality technique, synchronization criteria are derived for switched interval networks under the arbitrary switching rule, which are easy to verify in practice. Moreover, as an application, the proposed scheme is then applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.

  8. Carbon based prosthetic devices

    Energy Technology Data Exchange (ETDEWEB)

    Devlin, D.J.; Carroll, D.W.; Barbero, R.S.; Archuleta, T. [Los Alamos National Lab., NM (US); Klawitter, J.J.; Ogilvie, W.; Strzepa, P. [Ascension Orthopedics (US); Cook, S.D. [Tulane Univ., New Orleans, LA (US). School of Medicine

    1998-12-31

    This is the final report of a one-year, Laboratory Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The project objective was to evaluate the use of carbon/carbon-fiber-reinforced composites for use in endoprosthetic devices. The application of these materials for the metacarpophalangeal (MP) joints of the hand was investigated. Issues concerning mechanical properties, bone fixation, biocompatibility, and wear are discussed. A system consisting of fiber reinforced materials with a pyrolytic carbon matrix and diamond-like, carbon-coated wear surfaces was developed. Processes were developed for the chemical vapor infiltration (CVI) of pyrolytic carbon into porous fiber preforms with the ability to tailor the outer porosity of the device to provide a surface for bone in-growth. A method for coating diamond-like carbon (DLC) on the articulating surface by plasma-assisted chemical vapor deposition (CVD) was developed. Preliminary results on mechanical properties of the composite system are discussed and initial biocompatibility studies were performed.

  9. Neural prostheses and biomedical microsystems in neurological rehabilitation.

    Science.gov (United States)

    Koch, K P

    2007-01-01

    Interfaces between electrodes and the neural system differ with respect to material and shape depending on their intended application and fabrication method. This chapter will review the different electrode designs regarding the technological implementation and fabrication process. Furthermore this book chapter will describe electrodes for interfacing the peripheral nerves like cuff, book or helix as well as electrodes for interfacing the cortex like needle arrays. The implantation method and mechanical interaction between the electrode and the nervous tissue were taken into consideration. To develop appropriate microtechnological assembling strategies that ensure proper interfacing between the tiny electrodes and microelectronics or connectors is one of the major challenges. The integration of electronics into the system helps to improve the reliability of detecting neural signals and reduces the size of the implants. Promising results with these novel electrodes will pave the road for future developments such as visual prosthetics or improved control of artificial limbs in paralyzed patients.

  10. Consumer Guide for Amputees: A Guide to Lower Limb Prosthetics

    Science.gov (United States)

    Consumer Guide for Amputees: A Guide to Lower Limb Prosthetics: Part I -- Prosthetic Design: Basic Concepts Volume 8 · ... wanted to have available a comprehensive explanation of limb prosthetics written in easily understood language for amputee consumers. ...

  11. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images

    NARCIS (Netherlands)

    Grinsven, M.J.J.P. van; Ginneken, B. van; Hoyng, C.B.; Theelen, T.; Sanchez, C.I.

    2016-01-01

    Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image

  12. Rotationally actuated prosthetic helping hand

    Science.gov (United States)

    Norton, William E. (Inventor); Belcher, Jewell G., Jr. (Inventor); Carden, James R. (Inventor); West, Thomas W. (Inventor)

    1991-01-01

    A prosthetic device has been developed for below-the-elbow amputees. The device consists of a cuff, a stem, a housing, two hook-like fingers, an elastic band for holding the fingers together, and a brace. The fingers are pivotally mounted on a housing that is secured to the amputee's upper arm with the brace. The stem, which also contains a cam, is rotationally mounted within the housing and is secured to the cuff, which fits over the amputee's stump. By rotating the cammed stem between the fingers with the lower arm, the amputee can open and close the fingers.

  13. Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm. (On-Line Harmonics Estimation Application

    Directory of Open Access Journals (Sweden)

    Eyad K Almaita

    2017-03-01

    Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application.  International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17

  14. Application of viral vectors to the study of neural connectivities and neural circuits in the marmoset brain.

    Science.gov (United States)

    Watakabe, Akiya; Sadakane, Osamu; Hata, Katsusuke; Ohtsuka, Masanari; Takaji, Masafumi; Yamamori, Tetsuo

    2017-03-01

    It is important to study the neural connectivities and functions in primates. For this purpose, it is critical to be able to transfer genes to certain neurons in the primate brain so that we can image the neuronal signals and analyze the function of the transferred gene. Toward this end, our team has been developing gene transfer systems using viral vectors. In this review, we summarize our current achievements as follows. 1) We compared the features of gene transfer using five different AAV serotypes in combination with three different promoters, namely, CMV, mouse CaMKII (CaMKII), and human synapsin 1 (hSyn1), in the marmoset cortex with those in the mouse and macaque cortices. 2) We used target-specific double-infection techniques in combination with TET-ON and TET-OFF using lentiviral retrograde vectors for enhanced visualization of neural connections. 3) We used an AAV-mediated gene transfer method to study the transcriptional control for amplifying fluorescent signals using the TET/TRE system in the primate neocortex. We also established systems for shRNA mediated gene targeting in a neocortical region where a gene is significantly expressed and for expressing the gene using the CMV promoter for an unexpressed neocortical area in the primate cortex using AAV vectors to understand the regulation of downstream genes. Our findings have demonstrated the feasibility of using viral vector mediated gene transfer systems for the study of primate cortical circuits using the marmoset as an animal model. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 354-372, 2017. © 2016 The Authors. Developmental Neurobiology Published by Wiley Periodicals, Inc.

  15. EDITORIAL: Focus on the neural interface Focus on the neural interface

    Science.gov (United States)

    Durand, Dominique M.

    2009-10-01

    The possibility of an effective connection between neural tissue and computers has inspired scientists and engineers to develop new ways of controlling and obtaining information from the nervous system. These applications range from `brain hacking' to neural control of artificial limbs with brain signals. Notwithstanding the significant advances in neural prosthetics in the last few decades and the success of some stimulation devices such as cochlear prosthesis, neurotechnology remains below its potential for restoring neural function in patients with nervous system disorders. One of the reasons for this limited impact can be found at the neural interface and close attention to the integration between electrodes and tissue should improve the possibility of successful outcomes. The neural interfaces research community consists of investigators working in areas such as deep brain stimulation, functional neuromuscular/electrical stimulation, auditory prostheses, cortical prostheses, neuromodulation, microelectrode array technology, brain-computer/machine interfaces. Following the success of previous neuroprostheses and neural interfaces workshops, funding (from NIH) was obtained to establish a biennial conference in the area of neural interfaces. The first Neural Interfaces Conference took place in Cleveland, OH in 2008 and several topics from this conference have been selected for publication in this special section of the Journal of Neural Engineering. Three `perspectives' review the areas of neural regeneration (Corredor and Goldberg), cochlear implants (O'Leary et al) and neural prostheses (Anderson). Seven articles focus on various aspects of neural interfacing. One of the most popular of these areas is the field of brain-computer interfaces. Fraser et al, report on a method to generate robust control with simple signal processing algorithms of signals obtained with electrodes implanted in the brain. One problem with implanted electrode arrays, however, is that

  16. A training platform for many-dimensional prosthetic devices using a virtual reality environment.

    Science.gov (United States)

    Putrino, David; Wong, Yan T; Weiss, Adam; Pesaran, Bijan

    2015-04-15

    Brain machine interfaces (BMIs) have the potential to assist in the rehabilitation of millions of patients worldwide. Despite recent advancements in BMI technology for the restoration of lost motor function, a training environment to restore full control of the anatomical segments of an upper limb extremity has not yet been presented. Here, we develop a virtual upper limb prosthesis with 27 independent dimensions, the anatomical dimensions of the human arm and hand, and deploy the virtual prosthesis as an avatar in a virtual reality environment (VRE) that can be controlled in real-time. The prosthesis avatar accepts kinematic control inputs that can be captured from movements of the arm and hand as well as neural control inputs derived from processed neural signals. We characterize the system performance under kinematic control using a commercially available motion capture system. We also present the performance under kinematic control achieved by two non-human primates (Macaca Mulatta) trained to use the prosthetic avatar to perform reaching and grasping tasks. This is the first virtual prosthetic device that is capable of emulating all the anatomical movements of a healthy upper limb in real-time. Since the system accepts both neural and kinematic inputs for a variety of many-dimensional skeletons, we propose it provides a customizable training platform for the acquisition of many-dimensional neural prosthetic control. Copyright © 2014 Elsevier B.V. All rights reserved.

  17. Undergraduate prosthetics and orthotics teaching methods: A baseline for international comparison.

    Science.gov (United States)

    Aminian, Gholamreza; O'Toole, John M; Mehraban, Afsoon Hassani

    2015-08-01

    Education of Prosthetics and Orthotics is a relatively recent professional program. While there has been some work on various teaching methods and strategies in international medical education, limited publication exists within prosthetics and orthotics. To identify the teaching and learning methods that are used in Bachelor-level prosthetics and orthotics programs that are given highest priority by expert prosthetics and orthotics instructors from regions enjoying a range of economic development. Mixed method. The study partly documented by this article utilized a mixed method approach (qualitative and quantitative methods) within which each phase provided data for other phases. It began with analysis of prosthetics and orthotics curricula documents, which was followed by a broad survey of instructors in this field and then a modified Delphi process. The expert instructors who participated in this study gave high priority to student-centered, small group methods that encourage critical thinking and may lead to lifelong learning. Instructors from more developed nations placed higher priority on student's independent acquisition of prosthetics and orthotics knowledge, particularly in clinical training. Application of student-centered approaches to prosthetics and orthotics programs may be preferred by many experts, but there appeared to be regional differences in the priority given to different teaching methods. The results of this study identify the methods of teaching that are preferred by expert prosthetics and orthotics instructors from a variety of regions. This treatment of current instructional techniques may inform instructor choice of teaching methods that impact the quality of education and improve the professional skills of students. © The International Society for Prosthetics and Orthotics 2014.

  18. Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

    Directory of Open Access Journals (Sweden)

    Min Wang

    2017-01-01

    Full Text Available This paper focuses on neural learning from adaptive neural control (ANC for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE condition for neural networks (NNs. Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

  19. Combined application of mixture experimental design and artificial neural networks in the solid dispersion development.

    Science.gov (United States)

    Medarević, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djurić, Zorica; Ibrić, Svetlana

    2016-01-01

    This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.

  20. A Real Valued Neural Network Based Autoregressive Energy Detector for Cognitive Radio Application.

    Science.gov (United States)

    Onumanyi, A J; Onwuka, E N; Aibinu, A M; Ugweje, O C; Salami, M J E

    2014-01-01

    A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application.

  1. Applications of Wavelet Neural Network Model to Building Settlement Prediction: A Case Study

    Directory of Open Access Journals (Sweden)

    Qulin TAN

    2014-04-01

    Full Text Available Deformation monitoring is a significant work for engineering safety, which is performed throughout the entire process of engineering design, construction and operation. Based on the theoretic analysis of wavelet and neural network, we applied the improved BP neural network model, auxiliary wavelet neural network model and embedded wavelet neural network model to the settlement prediction in one practical engineering monitoring project with MATLAB software programming. The cumulative and the interval settlement was predicted and compared with measured data. The overall performances of the three models were analyzed and compared. The results show that the accuracies of two kinds of wavelet neural network models are roughly the same, which prediction errors of monitoring points are less than 1mm, obviously superior to the single BP neural network model.

  2. A neural networks application for the study of the influence of transport conditions on the working performance

    Science.gov (United States)

    Anghel, D.-C.; Ene, A.; Ştirbu, C.; Sicoe, G.

    2017-10-01

    This paper presents a study about the factors that influence the working performances of workers in the automotive industry. These factors regard mainly the transportations conditions, taking into account the fact that a large number of workers live in places that are far away of the enterprise. The quantitative data obtained from this study will be generalized by using a neural network, software simulated. The neural network is able to estimate the performance of workers even for the combinations of input factors that had been not recorded by the study. The experimental data obtained from the study will be divided in two classes. The first class that contains approximately 80% of data will be used by the Java software for the training of the neural network. The weights resulted from the training process will be saved in a text file. The other class that contains the rest of the 20% of experimental data will be used to validate the neural network. The training and the validation of the networks are performed in a Java software (TrainAndValidate java class). We designed another java class, Test.java that will be used with new input data, for new situations. The experimental data collected from the study. The software that simulated the neural network. The software that estimates the working performance, when new situations are met. This application is useful for human resources department of an enterprise. The output results are not quantitative. They are qualitative (from low performance to high performance, divided in five classes).

  3. A sensory feedback system for prosthetic hand based on evoked tactile sensation.

    Science.gov (United States)

    Liu, X X; Chai, G H; Qu, H E; Lan, N

    2015-01-01

    The lack of reliable sensory feedback has been one of the barriers in prosthetic hand development. Restoring sensory function from prosthetic hand to amputee remains a great challenge to neural engineering. In this paper, we present the development of a sensory feedback system based on the phenomenon of evoked tactile sensation (ETS) at the stump skin of residual limb induced by transcutaneous electrical nerve stimulation (TENS). The system could map a dynamic pattern of stimuli to an electrode placed on the corresponding projected finger areas on the stump skin. A pressure transducer placed at the tip of prosthetic fingers was used to sense contact pressure, and a high performance DSP processor sampled pressure signals, and calculated the amplitude of feedback stimulation in real-time. Biphasic and charge-balanced current pulses with amplitude modulation generated by a multi-channel laboratory stimulator were delivered to activate sensory nerves beneath the skin. We tested this sensory feedback system in amputee subjects. Preliminary results showed that the subjects could perceive different levels of pressure at the tip of prosthetic finger through evoked tactile sensation (ETS) with distinct grades and modalities. We demonstrated the feasibility to restore the perceptual sensation from prosthetic fingers to amputee based on the phenomenon of evoked tactile sensation (ETS) with TENS.

  4. Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

    Science.gov (United States)

    Bakanovskaya, L. N.

    2016-08-01

    The article touches upon building of a heart electrical axis position conclusion model using an artificial neural network. The input signals of the neural network are the values of deflections Q, R and S; and the output signal is the value of the heart electrical axis position. Training of the network is carried out by the error propagation method. The test results allow concluding that the created neural network makes a conclusion with a high degree of accuracy.

  5. An interpretation of neural networks as inference engines with application to transformer failure diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Adriana R. Garcez; Miranda, Vladimiro [Instituto de Engenharia de Sistemas e Computadores do Porto, INESC Porto (Portugal)

    2005-12-01

    An artificial neural network concept has been developed for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA). A new methodology for mapping the neural network into a rule-based inference system is described. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a Fuzzy Inference System. Some studies are reported, illustrating the good results obtained. (author)

  6. Design and evaluation of neural classifiers application to skin lesion classification

    DEFF Research Database (Denmark)

    Hintz-Madsen, Mads; Hansen, Lars Kai; Larsen, Jan

    1995-01-01

    Addresses design and evaluation of neural classifiers for the problem of skin lesion classification. By using Gauss Newton optimization for the entropic cost function in conjunction with pruning by Optimal Brain Damage and a new test error estimate, the authors show that this scheme is capable...... of optimizing the architecture of neural classifiers. Furthermore, error-reject tradeoff theory indicates, that the resulting neural classifiers for the skin lesion classification problem are near-optimal...

  7. Engineering Applications of Neural Computing: A State-of-the-Art Survey

    Science.gov (United States)

    1991-05-01

    Papachristou. C. A., "Training of a Neural Network for Pattern Classifi- cation Based on Entropy Measure," Proceedings of 1988 IEEE International Conference...diction and System Modeling," Technical Report LA-UR-87-2662, Los Alamos National Lab- oratoiy, 1987. 10. Levy, W. B., "Maximum Entropy Prediction in Neural...Lawrence Erlbaum, Hillsdale, NJ, 1990. 53. MacGregor, R. J., Neural and Brain Modeling, The Academic Press, New York, 1987. 54. Marr , D., Vision, San

  8. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  9. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.

    Science.gov (United States)

    Lancashire, Lee J; Lemetre, Christophe; Ball, Graham R

    2009-05-01

    Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

  10. An application of neural networks in microeconomics: input-output mapping in a power generation subsector of the US electricity industry

    NARCIS (Netherlands)

    Erbas, B.C.; Stefanou, S.E.

    2009-01-01

    The use of the artificial neural networks in economics and business goes back to 1950s, while the major bulk of the applications have been developed in more recent years. Reviewing this literature indicates that the field of business benefits from the neural networks in a wide spectrum from

  11. Real-time animation software for customized training to use motor prosthetic systems.

    Science.gov (United States)

    Davoodi, Rahman; Loeb, Gerald E

    2012-03-01

    Research on control of human movement and development of tools for restoration and rehabilitation of movement after spinal cord injury and amputation can benefit greatly from software tools for creating precisely timed animation sequences of human movement. Despite their ability to create sophisticated animation and high quality rendering, existing animation software are not adapted for application to neural prostheses and rehabilitation of human movement. We have developed a software tool known as MSMS (MusculoSkeletal Modeling Software) that can be used to develop models of human or prosthetic limbs and the objects with which they interact and to animate their movement using motion data from a variety of offline and online sources. The motion data can be read from a motion file containing synthesized motion data or recordings from a motion capture system. Alternatively, motion data can be streamed online from a real-time motion capture system, a physics-based simulation program, or any program that can produce real-time motion data. Further, animation sequences of daily life activities can be constructed using the intuitive user interface of Microsoft's PowerPoint software. The latter allows expert and nonexpert users alike to assemble primitive movements into a complex motion sequence with precise timing by simply arranging the order of the slides and editing their properties in PowerPoint. The resulting motion sequence can be played back in an open-loop manner for demonstration and training or in closed-loop virtual reality environments where the timing and speed of animation depends on user inputs. These versatile animation utilities can be used in any application that requires precisely timed animations but they are particularly suited for research and rehabilitation of movement disorders. MSMS's modeling and animation tools are routinely used in a number of research laboratories around the country to study the control of movement and to develop and test

  12. Application of Convolutional Neural Network in Classification of High Resolution Agricultural Remote Sensing Images

    Science.gov (United States)

    Yao, C.; Zhang, Y.; Zhang, Y.; Liu, H.

    2017-09-01

    With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

  13. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    Science.gov (United States)

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-01-01

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. PMID:27529225

  14. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    Directory of Open Access Journals (Sweden)

    Lucas Antón Pastur-Romay

    2016-08-01

    Full Text Available Over the past decade, Deep Artificial Neural Networks (DNNs have become the state-of-the-art algorithms in Machine Learning (ML, speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs. All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS, Quantitative Structure–Activity Relationship (QSAR research, protein structure prediction and genomics (and other omics data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

  15. Application of neural networks and other machine learning algorithms to DNA sequence analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lapedes, A.; Barnes, C.; Burks, C.; Farber, R.; Sirotkin, K.

    1988-01-01

    In this article we report initial, quantitative results on application of simple neutral networks, and simple machine learning methods, to two problems in DNA sequence analysis. The two problems we consider are: (1) determination of whether procaryotic and eucaryotic DNA sequences segments are translated to protein. An accuracy of 99.4% is reported for procaryotic DNA (E. coli) and 98.4% for eucaryotic DNA (H. Sapiens genes known to be expressed in liver); (2) determination of whether eucaryotic DNA sequence segments containing the dinucleotides ''AG'' or ''GT'' are transcribed to RNA splice junctions. Accuracy of 91.2% was achieved on intron/exon splice junctions (acceptor sites) and 92.8% on exon/intron splice junctions (donor sites). The solution of these two problems, by use of information processing algorithms operating on unannotated base sequences and without recourse to biological laboratory work, is relevant to the Human Genome Project. A variety of neural network, machine learning, and information theoretic algorithms are used. The accuracies obtained exceed those of previous investigations for which quantitative results are available in the literature. They result from an ongoing program of research that applies machine learning algorithms to the problem of determining biological function of DNA sequences. Some predictions of possible new genes using these methods are listed -- although a complete survey of the H. sapiens and E. coli sections of GenBank will be given elsewhere. 36 refs., 6 figs., 6 tabs.

  16. APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

    Directory of Open Access Journals (Sweden)

    C. Yao

    2017-09-01

    Full Text Available With the rapid development of Precision Agriculture (PA promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN. For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

  17. Artificial neural networks in gynaecological diseases: current and potential future applications.

    Science.gov (United States)

    Siristatidis, Charalampos S; Chrelias, Charalampos; Pouliakis, Abraham; Katsimanis, Evangelos; Kassanos, Dimitrios

    2010-10-01

    Current (and probably future) practice of medicine is mostly associated with prediction and accurate diagnosis. Especially in clinical practice, there is an increasing interest in constructing and using valid models of diagnosis and prediction. Artificial neural networks (ANNs) are mathematical systems being used as a prospective tool for reliable, flexible and quick assessment. They demonstrate high power in evaluating multifactorial data, assimilating information from multiple sources and detecting subtle and complex patterns. Their capability and difference from other statistical techniques lies in performing nonlinear statistical modelling. They represent a new alternative to logistic regression, which is the most commonly used method for developing predictive models for outcomes resulting from partitioning in medicine. In combination with the other non-algorithmic artificial intelligence techniques, they provide useful software engineering tools for the development of systems in quantitative medicine. Our paper first presents a brief introduction to ANNs, then, using what we consider the best available evidence through paradigms, we evaluate the ability of these networks to serve as first-line detection and prediction techniques in some of the most crucial fields in gynaecology. Finally, through the analysis of their current application, we explore their dynamics for future use.

  18. Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables.

    Science.gov (United States)

    Date, Yasuhiro; Kikuchi, Jun

    2018-02-06

    Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because they have difficulty in identifying contribution factors, e.g., biomarkers, in constructed classification and regression models. In this paper, we describe an improved DNN-based analytical approach that incorporates an importance estimation for each variable using a mean decrease accuracy (MDA) calculation, which is based on a permutation algorithm; this approach is called DNN-MDA. The performance of the DNN-MDA approach was evaluated using a data set of metabolic profiles derived from yellowfin goby that lived in various rivers throughout Japan. Its performance was compared with that of conventional multivariate and machine learning methods, and the DNN-MDA approach was found to have the best classification accuracy (97.8%) among the examined methods. In addition to this, the DNN-MDA approach facilitated the identification of important variables such as trimethylamine N-oxide, inosinic acid, and glycine, which were characteristic metabolites that contributed to the discrimination of the geographical differences between fish caught in the Kanto region and those caught in other regions. As a result, the DNN-MDA approach is a useful and powerful tool for determining the geographical origins of specimens and identifying their biomarkers in metabolomics studies that are conducted in biological and environmental systems.

  19. Prospect of Human Pluripotent Stem Cell-Derived Neural Crest Stem Cells in Clinical Application

    Directory of Open Access Journals (Sweden)

    Qian Zhu

    2016-01-01

    Full Text Available Neural crest stem cells (NCSCs represent a transient and multipotent cell population that contributes to numerous anatomical structures such as peripheral nervous system, teeth, and cornea. NCSC maldevelopment is related to various human diseases including pigmentation abnormalities, disorders affecting autonomic nervous system, and malformations of teeth, eyes, and hearts. As human pluripotent stem cells including human embryonic stem cells (hESCs and human induced pluripotent stem cells (hiPSCs can serve as an unlimited cell source to generate NCSCs, hESC/hiPSC-derived NCSCs can be a valuable tool to study the underlying mechanisms of NCSC-associated diseases, which paves the way for future therapies for these abnormalities. In addition, hESC/hiPSC-derived NCSCs with the capability of differentiating to various cell types are highly promising for clinical organ repair and regeneration. In this review, we first discuss NCSC generation methods from human pluripotent stem cells and differentiation mechanism of NCSCs. Then we focus on the clinical application potential of hESC/hiPSC-derived NCSCs on peripheral nerve injuries, corneal blindness, tooth regeneration, pathological melanogenesis, Hirschsprung disease, and cardiac repair and regeneration.

  20. Expandable and Rapidly Differentiating Human Induced Neural Stem Cell Lines for Multiple Tissue Engineering Applications

    Directory of Open Access Journals (Sweden)

    Dana M. Cairns

    2016-09-01

    Full Text Available Limited availability of human neurons poses a significant barrier to progress in biological and preclinical studies of the human nervous system. Current stem cell-based approaches of neuron generation are still hindered by prolonged culture requirements, protocol complexity, and variability in neuronal differentiation. Here we establish stable human induced neural stem cell (hiNSC lines through the direct reprogramming of neonatal fibroblasts and adult adipose-derived stem cells. These hiNSCs can be passaged indefinitely and cryopreserved as colonies. Independently of media composition, hiNSCs robustly differentiate into TUJ1-positive neurons within 4 days, making them ideal for innervated co-cultures. In vivo, hiNSCs migrate, engraft, and contribute to both central and peripheral nervous systems. Lastly, we demonstrate utility of hiNSCs in a 3D human brain model. This method provides a valuable interdisciplinary tool that could be used to develop drug screening applications as well as patient-specific disease models related to disorders of innervation and the brain.

  1. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.

    Science.gov (United States)

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-08-11

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

  2. 21 CFR 890.3420 - External limb prosthetic component.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false External limb prosthetic component. 890.3420... External limb prosthetic component. (a) Identification. An external limb prosthetic component is a device... total prosthesis. Examples of external limb prosthetic components include the following: Ankle, foot...

  3. 38 CFR 17.149 - Sensori-neural aids.

    Science.gov (United States)

    2010-07-01

    ... medical treatment. (c) VA will furnish needed hearing aids to those veterans who have service-connected... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Sensori-neural aids. 17... Prosthetic, Sensory, and Rehabilitative Aids § 17.149 Sensori-neural aids. (a) Notwithstanding any other...

  4. Development of prosthetic arm with pneumatic prosthetic hand and tendon-driven wrist.

    Science.gov (United States)

    Takeda, Hiroyuki; Tsujiuchi, Nobutaka; Koizumi, Takayuki; Kan, Hiroto; Hirano, Masanori; Nakamura, Yoichiro

    2009-01-01

    Recently, various prosthetic arms have been developed, but few are both attractive and functional. Considering human coexistence, prosthetic arms must be both safe and flexible. In this research, we developed a novel prosthetic arm with a five-fingered prosthetic hand using our original pneumatic actuators and a slender tendon-driven wrist using a wire drive and two small motors. Because the prosthetic hand's driving source is comprised of small pneumatic actuators, the prosthetic hand is safe when it makes contact with people; it can also operate flexibly. In addition, the arm has a tendon-driven wrist to expand its motion space and to perform many operations. First, we explain the pneumatic hand's drive mechanism and its tendon-driven wrist. Next, we identify the characteristics of the hand and the wrist and construct a control system for this arm and verify its control performance.

  5. Application of an artificial neural network to ready-mixed concretes mix design

    Directory of Open Access Journals (Sweden)

    Setién, J.

    2003-06-01

    Full Text Available This paper presents the practical application of cm artificial neural network (ANN to the problem of concrete mix in a factory. After a brief introduction to the complex problem of concrete mixes design and a quick review of the fundamental basis of neurocomputation, an optimal neural network model has been developed to cope with such a problem. For training the net, several control mixes have been fabricated recording in all cases both the characteristic 28 days compressive strength and the workability measured in terms of the slump of the Abrams' cone. After the training process of the net, the power of its predictive ability is checked by comparison of the results obtained with those corresponding to four reference mixes; in this way, it is shown that the considered approach can be used in multicriterial search for optimal concrete mixes.

    En este trabajo se presenta la aplicación práctica de una red neuronal artificial (ANN al problema de la dosificación de hormigones en planta. Tras una breve introducción a la compleja problemática de la dosificación de hormigones y un repaso a los fundamentos de la neurocomputación, se diseña un modelo de red neuronal óptimo para abordar el problema. Para entrenar dicha red, se realizan varias amasadas de prueba, registrándose para cada una de ellas la trabajabilidad, mediante la medida del asiento del cono de Abrams, y ¡a resistencia característica a los 28 días. Una vez entrenada la red, se pone a prueba su carácter predictivo comparando los resultados que proporciona con los de cuatro amasadas de referencia, demostrándose que esta aproximación puede ser utilizada como método multicriterial para la obtención de mezclas óptimas de hormigón.

  6. The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

    Science.gov (United States)

    Wang, Pengbo

    2017-11-01

    In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.

  7. Neural network for solving Nash equilibrium problem in application of multiuser power control.

    Science.gov (United States)

    He, Xing; Yu, Junzhi; Huang, Tingwen; Li, Chuandong; Li, Chaojie

    2014-09-01

    In this paper, based on an equivalent mixed linear complementarity problem, we propose a neural network to solve multiuser power control optimization problems (MPCOP), which is modeled as the noncooperative Nash game in modern digital subscriber line (DSL). If the channel crosstalk coefficients matrix is positive semidefinite, it is shown that the proposed neural network is stable in the sense of Lyapunov and global convergence to a Nash equilibrium, and the Nash equilibrium is unique if the channel crosstalk coefficients matrix is positive definite. Finally, simulation results on two numerical examples show the effectiveness and performance of the proposed neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. The application of neural network PID controller to control the light gasoline etherification

    Science.gov (United States)

    Cheng, Huanxin; Zhang, Yimin; Kong, Lingling; Meng, Xiangyong

    2017-06-01

    Light gasoline etherification technology can effectively improve the quality of gasoline, which is environmental- friendly and economical. By combining BP neural network and PID control and using BP neural network self-learning ability for online parameter tuning, this method optimizes the parameters of PID controller and applies this to the Fcc gas flow control to achieve the control of the final product- heavy oil concentration. Finally, through MATLAB simulation, it is found that the PID control based on BP neural network has better controlling effect than traditional PID control.

  9. Application of neural network for real-time measurement of electrical resistivity in cold crucible

    Science.gov (United States)

    Votava, Pavel; Poznyak, Igor

    2017-08-01

    The article describes use of an Induction furnace with cold crucible as a tool for real-time measurement of a melted material electrical resistivity. The measurement is based on an inverse problem solution of a 2D mathematical model, possibly implementable in a microcontroller or a FPGA in a form of a neural network. The 2D mathematical model results has been provided as a training set for the neural network. At the end, the implementation results are discussed together with uncertainty of measurement, which is done by the neural network implementation itself.

  10. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  11. Application Of Artificial Neural Networks In Modeling Of Manufactured Front Metallization Contact Resistance For Silicon Solar Cells

    Directory of Open Access Journals (Sweden)

    Musztyfaga-Staszuk M.

    2015-09-01

    Full Text Available This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace and unconventional (2. Selective Laser Sintering. Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM. Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

  12. Advanced upper limb prosthetic devices: implications for upper limb prosthetic rehabilitation.

    Science.gov (United States)

    Resnik, Linda; Meucci, Marissa R; Lieberman-Klinger, Shana; Fantini, Christopher; Kelty, Debra L; Disla, Roxanne; Sasson, Nicole

    2012-04-01

    The number of catastrophic injuries caused by improvised explosive devices in the Afghanistan and Iraq Wars has increased public, legislative, and research attention to upper limb amputation. The Department of Veterans Affairs (VA) has partnered with the Defense Advanced Research Projects Agency and DEKA Integrated Solutions to optimize the function of an advanced prosthetic arm system that will enable greater independence and function. In this special communication, we examine current practices in prosthetic rehabilitation including trends in adoption and use of prosthetic devices, financial considerations, and the role of rehabilitation team members in light of our experiences with a prototype advanced upper limb prosthesis during a VA study to optimize the device. We discuss key challenges in the adoption of advanced prosthetic technology and make recommendations for service provision and use of advanced upper limb prosthetics. Rates of prosthetic rejection are high among upper limb amputees. However, these rates may be reduced with sufficient training by a highly specialized, multidisciplinary team of clinicians, and a focus on patient education and empowerment throughout the rehabilitation process. There are significant challenges emerging that are unique to implementing the use of advanced upper limb prosthetic technology, and a lack of evidence to establish clinical guidelines regarding prosthetic prescription and treatment. Finally, we make recommendations for future research to aid in the identification of best practices and development of policy decisions regarding insurance coverage of prosthetic rehabilitation. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  13. Prosthetic vision: devices, patient outcomes and retinal research.

    Science.gov (United States)

    Hadjinicolaou, Alex E; Meffin, Hamish; Maturana, Matias I; Cloherty, Shaun L; Ibbotson, Michael R

    2015-09-01

    Retinal disease and its associated retinal degeneration can lead to the loss of photoreceptors and therefore, profound blindness. While retinal degeneration destroys the photoreceptors, the neural circuits that convey information from the eye to the brain are sufficiently preserved to make it possible to restore sight using prosthetic devices. Typically, these devices consist of a digital camera and an implantable neurostimulator. The image sensor in a digital camera has the same spatiotopic arrangement as the photoreceptors of the retina. Therefore, it is possible to extract meaningful spatial information from an image and deliver it via an array of stimulating electrodes directly to the surviving retinal circuits. Here, we review the structure and function of normal and degenerate retina. The different approaches to prosthetic implant design are described in the context of human and preclinical trials. In the last section, we review studies of electrical properties of the retina and its response to electrical stimulation. These types of investigation are currently assessing a number of key challenges identified in human trials, including stimulation efficacy, spatial localisation, desensitisation to repetitive stimulation and selective activation of retinal cell populations. © 2015 The Authors. Clinical and Experimental Optometry © 2015 Optometry Australia.

  14. Lymphatic opacification in the prosthetic hip

    Energy Technology Data Exchange (ETDEWEB)

    Bloom, R.A.; Gheorghiu, D. (Hadassah Univ. Hospital, Jerusalem (Israel). Dept. of Radiology); Krausz, Y. (Hadassah Univ. Hospital, Jerusalem (Israel). Dept. of Nuclear Medicine)

    1991-01-01

    A retrospective analysis of 52 patients with hip pain following total hip replacement was made. Each of them was evaluated by plain radiographs, technetium 99m pyrophosphate scans, arthrography with plain film substraction technique, and culture of joint fluid. In 30 cases there was evidence of prosthetic loosening, and in 21 of these lymphangeal opacification during arthrography was seen. In 15 cases with lymphongeal opacification the daignosis of prosthetic loosening was subsequently confirmed by prosthetic revision. In none of the 22 cases in which no evidence of prosthetic loosening was seen was there lymphatic opacification. It is concluded that lymphatic opacification during arthrography for pain following total hip prosthesis is a valuable ancillary sign of loosening. (orig.).

  15. Computed Tomography of Prosthetic Heart Valves

    NARCIS (Netherlands)

    Habets, J.

    2012-01-01

    Prosthetic heart valve (PHV) dysfunction is an infrequent but potentially life-threatening disease with a heterogeneous clinical presentation. Patients with PHV dysfunction clinically can present with symptoms of congestive heart failure (dyspnea, fatigue, edema), fever, angina pectoris, dizziness

  16. Successful Thrombolysis of Aortic Prosthetic Valve Thrombosis ...

    African Journals Online (AJOL)

    Arun Kumar Agnihotri

    threatening. Standard surgical treatment using cardiopulmonary bypass carries high maternal and fetal complications. Here we report a case of an antenatal female in first trimester with aortic prosthetic valve thrombosis (PVT), who was successfully ...

  17. DME Prosthetics Orthotics, and Supplies Fee Schedule

    Data.gov (United States)

    U.S. Department of Health & Human Services — Durable Medical Equipment, Prosthetics-Orthotics, and Supplies Fee Schedule. The list contains the fee schedule amounts, floors, and ceilings for all procedure codes...

  18. Overview: Mechanism and Control of a Prosthetic Arm.

    Science.gov (United States)

    Kulkarni, Tushar; Uddanwadiker, Rashmi

    2015-09-01

    Continuous growth in industrialization and lack of awareness in safety parameters the cases of amputations are growing. The search of safer, simpler and automated prosthetic arms for managing upper limbs is expected. Continuous efforts have been made to design and develop prosthetic arms ranging from simple harness actuated to automated mechanisms with various control options. However due the cost constraints, the automated prosthetic arms are still out of the reach of needy people. Recent data have shown that there is a wide scope to develop a low cost and light weight upper limb prosthesis. This review summarizes the various designs methodologies, mechanisms and control system developed by the researchers and the advances therein. Educating the patient to develop acceptability to prosthesis and using the same for the most basic desired functions of human hand, post amputation care and to improve patient's independent life is equally important. In conclusion it can be interpreted that there is a wide scope in design in an adaptive mechanism for opening and closing of the fingers using other methods of path and position synthesis. Simple mechanisms and less parts may optimize the cost factor. Reduction in the weight of the prosthesis may be achieved using polymers used for engineering applications. Control system will remain never ending challenge for the researchers, but it is essential to maintain the simplicity from the patients perspective.

  19. High-density percutaneous chronic connector for neural prosthetics

    Energy Technology Data Exchange (ETDEWEB)

    Shah, Kedar G.; Bennett, William J.; Pannu, Satinderpall S.

    2015-09-22

    A high density percutaneous chronic connector, having first and second connector structures each having an array of magnets surrounding a mounting cavity. A first electrical feedthrough array is seated in the mounting cavity of the first connector structure and a second electrical feedthrough array is seated in the mounting cavity of the second connector structure, with a feedthrough interconnect matrix positioned between a top side of the first electrical feedthrough array and a bottom side of the second electrical feedthrough array to electrically connect the first electrical feedthrough array to the second electrical feedthrough array. The two arrays of magnets are arranged to attract in a first angular position which connects the first and second connector structures together and electrically connects the percutaneously connected device to the external electronics, and to repel in a second angular position to facilitate removal of the second connector structure from the first connector structure.

  20. Adaptive control using a hybrid-neural model: application to a polymerisation reactor

    Directory of Open Access Journals (Sweden)

    Cubillos F.

    2001-01-01

    Full Text Available This work presents the use of a hybrid-neural model for predictive control of a plug flow polymerisation reactor. The hybrid-neural model (HNM is based on fundamental conservation laws associated with a neural network (NN used to model the uncertain parameters. By simulations, the performance of this approach was studied for a peroxide-initiated styrene tubular reactor. The HNM was synthesised for a CSTR reactor with a radial basis function neural net (RBFN used to estimate the reaction rates recursively. The adaptive HNM was incorporated in two model predictive control strategies, a direct synthesis scheme and an optimum steady state scheme. Tests for servo and regulator control showed excellent behaviour following different setpoint variations, and rejecting perturbations. The good generalisation and training capacities of hybrid models, associated with the simplicity and robustness characteristics of the MPC formulations, make an attractive combination for the control of a polymerisation reactor.

  1. Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Nanda

    2011-01-01

    Full Text Available Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN, polynomial perceptron network (PPN, and Legendre neural network (LeNN were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

  2. Surgical and Prosthetic Rehabilitation of Combination Syndrome

    Directory of Open Access Journals (Sweden)

    Paolo Carlino

    2014-01-01

    Full Text Available The aim of this report is to analyze the clinical symptoms, ethologic factors, and prosthetic rehabilitation in a case of Combination Syndrome (CS. The treatment of CS can be conventional or surgical, with or without the bone reconstruction of maxilla. The correct prosthetic treatment helps this kind of patients to restore the physiologic occlusion plane to allow a correct masticatory and aesthetic function. Management of this kind of patients can be a challenge for a dental practitioner.

  3. Artificial Neural Systems Application to the Simulation of Air Combat Decision Making

    Science.gov (United States)

    1992-04-01

    unit, the CPU , whereas neural networks utilize the effects of many, simple processing elements. Traditional computing is done in a step-by-step, serial...Nielsen Neurocomputers (HNC). The ANZA-Plus coprocessor is part of an 80386 -based computer system which is optimized for training and executing neural...host computer for this program is a Zenith 386/16 system running under the DOS 3.31 operating system. The 80386 microprocessor in this machine operates

  4. Neural Network Expert System in the Application of Tower Fault Diagnosis

    Science.gov (United States)

    Liu, Xiaoyang; Xia, Zhongwu; Tao, Zhiyong; Zhao, Zhenlian

    For the corresponding fuzzy relationship between the fault symptoms and the fault causes in the process of tower crane operation, this paper puts forward a kind of rapid new method of fast detection and diagnosis for common fault based on neural network expert system. This paper makes full use of expert system and neural network advantages, and briefly introduces the structure, function, algorithm and realization of the adopted system. Results show that the new algorithm is feasible and can achieve rapid faults diagnosis.

  5. Neural network computation for the evaluation of process rendering: application to thermally sprayed coatings

    Directory of Open Access Journals (Sweden)

    Guessasma Sofiane

    2017-01-01

    Full Text Available In this work, neural network computation is attempted to relate alumina and titania phase changes of a coating microstructure with respect to energetic parameters of atmospheric plasma straying (APS process. Experimental results were analysed using standard fitting routines and neural computation to quantify the effect of arc current, hydrogen ratio and total plasma flow rate. For a large parameter domain, phase changes were 10% for alumina and 8% for titania with a significant control of titania phase.

  6. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Application of neural networks to determine moisture content on humidity-attenuated NIR spectra

    Energy Technology Data Exchange (ETDEWEB)

    Lopez, T. [Westinghouse Hanford Company, Richland, WA (United States); Philipp, B.L. [ICF Kaiser, Richland, WA (United States); Thompson-Bachmeier, S. [Washington State Univ., Pullman, WA (United States)

    1995-12-31

    Moisture has been identified as one of the critical tank waste parameters that impacts the safety status of the wastes, particularly tanks containing ferro/ferricyanide materials. Since water content is affected by a number of factors, including gravity, one hypothesis, currently being tested by Westinghouse Hanford`s Waste Tank Safety organization, is that the surface of the waste contains a minimum of water compared to the material deeper in the tank. Assuming this hypothesis is correct, a minimum internal waste water content will be obtained by measuring the surface water content. Near infrared analysis is a nondestructive technique that takes advantage of the tendency of water molecules to absorb specific wavelengths of NIR energy. When a sample containing water is exposed to those wavelengths, a certain portion of the energy will be absorbed by the water, and the remainder will be reflected. By measuring the reflected energy, the concentration of water in the sample can be determined. An initial investigation into the feasibility of remote sensing for hot cell and waste tank applications was performed at the University of Washington`s Center for Process Analytical Chemistry (CPAC) under the direction of Westinghouse Hanford Company. The BY-104 waste tank simulant test data showed that for these samples, ten percent of the incident radiation is scattered. When collected, this signal is available for determining moisture content because the moisture content of the waste affects the scattering. However, atmospheric relative humidity causes a signal attenuation that will impact any in situ measurements being obtained. For simulation, this spectra was used along with software generated atmospheric transmission data from 0-60 meters to produce a modified sample set. These data are analyzed using a backpropagation neural network algorithm to construct a model that would predict surface moisture content.

  8. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

    Science.gov (United States)

    Hirasawa, Toshiaki; Aoyama, Kazuharu; Tanimoto, Tetsuya; Ishihara, Soichiro; Shichijo, Satoki; Ozawa, Tsuyoshi; Ohnishi, Tatsuya; Fujishiro, Mitsuhiro; Matsuo, Keigo; Fujisaki, Junko; Tada, Tomohiro

    2018-01-15

    Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN. The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface. The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

  9. Kinematics in the terminal swing phase of unilateral transfemoral amputees: microprocessor-controlled versus swing-phase control prosthetic knees.

    Science.gov (United States)

    Mâaref, Khaled; Martinet, Noël; Grumillier, Constance; Ghannouchi, Slaheddine; André, Jean Marie; Paysant, Jean

    2010-06-01

    To analyze the spatiotemporal parameters in the terminal swing phase of the prosthetic limb in unilateral transfemoral amputees (TFAs) compared with a group of asymptomatic subjects, and to identify a latency period (LP) in the TFA between the full extension of the prosthetic knee and the initial ground contact of the ipsilateral foot. To study the correlation between the LP and the duration of the swing phase. To evaluate the influence of the type of knee, the time since amputation, and the amputation level on the latency period. Three-dimensional gait analysis with an optoelectronic device. Gait analysis laboratory of a re-education and functional rehabilitation service. TFA (n=29) and able-bodied (n=15) subjects. Not applicable. Spatiotemporal and kinematics gait parameters. The swing phase and the LP of the prosthetic limb, associated with a consequently longer single-limb stance phase in the intact limb, were significantly longer than those measured in the intact limbs of these subjects, as well as those measured on both lower limbs of the able-bodied subjects (Pprosthetic side. The LP measured in the prosthetic limb of TFA with a swing-phase control prosthetic knee is significantly greater than in those using the microprocessor-controlled prosthetic knee (Plimb of TFA, the LP is significantly greater in the prosthetic limb. It can explain the lengthened swing phase on the prosthetic side of those subjects. The use of a microprocessor-controlled prosthetic knee allows the LP to be reduced. This LP appears to be necessary to insure the stability of the prosthetic knee. We suggest calling this time "confidence time." Copyright 2010 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  10. Myoelectric control of prosthetic hands: state-of-the-art review.

    Science.gov (United States)

    Geethanjali, Purushothaman

    2016-01-01

    Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy.

  11. Myoelectric control of prosthetic hands: state-of-the-art review

    Directory of Open Access Journals (Sweden)

    Geethanjali P

    2016-07-01

    Full Text Available Purushothaman Geethanjali School of Electrical Engineering Department of Control and Automation VIT University, Vellore, Tamil Nadu, India Abstract: Myoelectric signals (MES have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy. Keywords: EMG, assistive device, amputee, myoelectric control, electric powered, body ­powered, bioelectric signal control

  12. Identification of dynamic load for prosthetic structures.

    Science.gov (United States)

    Zhang, Dequan; Han, Xu; Zhang, Zhongpu; Liu, Jie; Jiang, Chao; Yoda, Nobuhiro; Meng, Xianghua; Li, Qing

    2017-12-01

    Dynamic load exists in numerous biomechanical systems, and its identification signifies a critical issue for characterizing dynamic behaviors and studying biomechanical consequence of the systems. This study aims to identify dynamic load in the dental prosthetic structures, namely, 3-unit implant-supported fixed partial denture (I-FPD) and teeth-supported fixed partial denture. The 3-dimensional finite element models were constructed through specific patient's computerized tomography images. A forward algorithm and regularization technique were developed for identifying dynamic load. To verify the effectiveness of the identification method proposed, the I-FPD and teeth-supported fixed partial denture structures were investigated to determine the dynamic loads. For validating the results of inverse identification, an experimental force-measuring system was developed by using a 3-dimensional piezoelectric transducer to measure the dynamic load in the I-FPD structure in vivo. The computationally identified loads were presented with different noise levels to determine their influence on the identification accuracy. The errors between the measured load and identified counterpart were calculated for evaluating the practical applicability of the proposed procedure in biomechanical engineering. This study is expected to serve as a demonstrative role in identifying dynamic loading in biomedical systems, where a direct in vivo measurement may be rather demanding in some areas of interest clinically. Copyright © 2017 John Wiley & Sons, Ltd.

  13. Cetacean Swimming with Prosthetic Limbs

    Science.gov (United States)

    Bode-Oke, Ayodeji; Ren, Yan; Dong, Haibo; Fish, Frank

    2016-11-01

    During entanglement in fishing gear, dolphins can suffer abrasions and amputations of flukes and fins. As a result, if the dolphin survives the ordeal, swimming performance is altered. Current rehabilitation technques is the use of prosthesis to regain swimming ability. In this work, analyses are focused on two dolphins with locomotive impairment; Winter (currently living in Clearwater Marine Aquarium in Florida) and Fuji (lived in Okinawa Churaumi Aquarium in Japan). Fuji lost about 75% of its fluke surface to necrosis (death of cells) and Winter lost its tail due to amputation. Both dolphins are aided by prosthetic tails that mimic the shape of a real dolphin tail. Using 3D surface reconstruction techniques and a high fidelity Computational Fluid Dynamics (CFD) flow solver, we were able to elucidate the kinematics and hydrodynamics and fluke deformation of these swimmers to clarify the effectiveness of prostheses in helping the dolphins regain their swimming ability. Associated with the performance, we identified distinct features in the wake structures that can explain this gap in the performance compared to a healthy dolphin. This work was supported by ONR MURI Grant Number N00014-14-1-0533.

  14. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    Science.gov (United States)

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  15. Differentiation of Equine Mesenchymal Stromal Cells into Cells of Neural Lineage: Potential for Clinical Applications

    Directory of Open Access Journals (Sweden)

    Claudia Cruz Villagrán

    2014-01-01

    Full Text Available Mesenchymal stromal cells (MSCs are able to differentiate into extramesodermal lineages, including neurons. Positive outcomes were obtained after transplantation of neurally induced MSCs in laboratory animals after nerve injury, but this is unknown in horses. Our objectives were to test the ability of equine MSCs to differentiate into cells of neural lineage in vitro, to assess differences in morphology and lineage-specific protein expression, and to investigate if horse age and cell passage number affected the ability to achieve differentiation. Bone marrow-derived MSCs were obtained from young and adult horses. Following demonstration of stemness, MSCs were neurally induced and microscopically assessed at different time points. Results showed that commercially available nitrogen-coated tissue culture plates supported proliferation and differentiation. Morphological changes were immediate and all the cells displayed a neural crest-like cell phenotype. Expression of neural progenitor proteins, was assessed via western blot or immunofluorescence. In our study, MSCs generated from young and middle-aged horses did not show differences in their ability to undergo differentiation. The effect of cell passage number, however, is inconsistent and further experiments are needed. Ongoing work is aimed at transdifferentiating these cells into Schwann cells for transplantation into a peripheral nerve injury model in horses.

  16. Application of the Wire Bonding Technology to a Flexible Neural Probe

    Science.gov (United States)

    Shimizu, Koichi; Nakanishi, Motofumi; Makikawa, Masaaki; Asajima, Syuzo; Konishi, Satoshi

    This paper proposes a novel neural probe using flexible metal wire for wire bonding on LSI chip. Wire bonding technology can provide a number of flexible wire arrays. The proposed neural probe is used for a nerve interface for functional electric stimulation (FES) technology which assists the paralysis of living body function by a spinal cord injury. The flexibility of probe will provide low invasive and safe neural interfaces for the nerve tissue from a long term view. We employ a combination of wire bonding and laser machining for the fabrication of aligned flexible probes. Aligned bonded flexible metal wires on electrodes are converted to probe arrays by cutting the bridge between electrodes. Typical dimension of a bonding wire is several tens μm in diameter and is suitable for neural probe to be inserted into nerve bundles. Needle shape is formed by electro-polishing of cut edge. Proposed method can be benefited by advantages of wire bonding as the widespread technology in electronics industry. Developed flexible neural probe based on the proposed technology is estimated as a nerve interface by inserting to a sciatic nerve of a rat.

  17. Percutaneous management of prosthetic valve thrombosis.

    Science.gov (United States)

    Hariram, Vuppaladadhiam

    2014-01-01

    Thrombosis of a prosthetic valve is a serious complication in patients with prosthetic heart valves. Thrombolysis is the initial choice of treatment. Patients who do not respond to thrombolysis are subjected to surgery which carries a high risk. We report a case series of 5 patients with prosthetic mitral valve thrombosis who did not respond to thrombolysis and were subjected to percutaneous manipulation of the prosthetic valves successfully and improved. Five patients who were diagnosed to have prosthetic mitral valve thrombosis and failed to respond to a minimum of 36 h of thrombolysis (persistent symptoms with increased gradients, abnormal findings on fluoroscopy),were subjected to percutaneous treatment after receiving proper consent. None of them had a visible thrombus on transthoracic echocardiogram. All patients underwent transseptal puncture following which a 6F JR4 guiding catheter was passed into the left atrium. The valve leaflets were repeatedly hit gently under fluoroscopic guidance till they regained their normal mobility. Mean age was 38.8 years. Average peak and mean gradients prior to the procedure were 38 and 25 and after the procedure were 12 and 6 mm of Hg respectively. All patients had successful recovery of valve motion on fluoroscopy with normalization of gradients and complete resolution of symptoms. None of the patients had any focal neurological deficits, embolic manifestations or bleeding complications. Percutaneous manipulation of prosthetic valves in selected patients with prosthetic valve thrombosis who do not respond to thrombolytic therapy is feasible and can be used as an alternative to surgery. Copyright © 2014 Cardiological Society of India. Published by Elsevier B.V. All rights reserved.

  18. APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF AIR POLLUTION LEVELS IN ENVIRONMENTAL MONITORING

    Directory of Open Access Journals (Sweden)

    Małgorzata Pawul

    2016-09-01

    Full Text Available Recently, a lot of attention was paid to the improvement of methods which are used to air quality forecasting. Artificial neural networks can be applied to model these problems. Their advantage is that they can solve the problem in the conditions of incomplete information, without the knowledge of the analytical relationship between the input and output data. In this paper we applied artificial neural networks to predict the PM 10 concentrations as factors determining the occurrence of smog phenomena. To create these networks we used meteorological data and concentrations of PM 10. The data were recorded in 2014 and 2015 at three measuring stations operating in Krakow under the State Environmental Monitoring. The best results were obtained by three-layer perceptron with back-propagation algorithm. The neural networks received a good fit in all cases.

  19. High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

    Science.gov (United States)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Jörg

    2011-04-01

    Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.

  20. Application of artificial neural networks to predict the deflections of reinforced concrete beams

    Science.gov (United States)

    Kaczmarek, Mateusz; Szymańska, Agnieszka

    2016-06-01

    Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

  1. Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

    Science.gov (United States)

    Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

    2017-03-01

    A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  2. Application of artificial neural networks with backpropagation technique in the financial data

    Science.gov (United States)

    Jaiswal, Jitendra Kumar; Das, Raja

    2017-11-01

    The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.

  3. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations.

    Science.gov (United States)

    Greenwald, Elliot; Masters, Matthew R; Thakor, Nitish V

    2016-01-01

    A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.

  4. The application of artificial neural networks for discrete wavelength retrievals of atmospheric nitrogen dioxide from space

    Science.gov (United States)

    Lawrence, J.; Singh Anand, J.; Leigh, R.; Chang, M.; Monks, P. S.

    2012-12-01

    Despite emission reductions in Europe, air quality continues to be a major health and policy issue. Significant areas of uncertainty persist in relating emissions, atmospheric composition and human exposure within the urban atmosphere. Furthermore, air quality continues to worsen in some highly populated parts of the world. The current air quality monitoring framework is based upon bottom-up emission estimates coupled with sparse in situ monitoring. Research at the University of Leicester in the UK is being conducted to investigate the feasibility of using a technique of discrete wavelength sunlight spectroscopy to derive concentrations of the pollutant nitrogen dioxide from a satellite platform. This technique has the potential to enable very light and compact instrumentation and may subsequently provide abundant air quality data of significant value to users and policy makers. A back propagation multi-layered perceptron artificial neural network (ANN) has been developed to retrieve atmospheric slant columns of nitrogen dioxide from simulated measurements. The ANN approach enables retrievals to be performed much faster than other retrieval methods once the network has been appropriately trained, which is a particularly useful feature in instances where a large quantity of retrievals is required in near real time. To generate the required training data for the ANN to understand the necessary relationships a radiative transfer model SCIATRAN was run to provide millions of spectral intensities and slant column concentrations. To enable the radiative transfer simulations to realistically portray urban air quality the SCIATRAN model was fed atmospheric profile and aerosol data from modelled air quality forecasts over London to enable assimilation of the atmospheric composition of a typical urban environment. The training data produced by SCIATRAN was configured to span a range of solar azimuth and zenith angles to provide results which are applicable to all low earth

  5. The Role of Virtual Articulator in Prosthetic and Restorative Dentistry

    Science.gov (United States)

    Aljanakh, Mohammad

    2014-01-01

    Virtual reality is a computer based technology linked with the future of dentistry and dental practice. The virtual articulator is one such application in prosthetic and restorative dentistry based on virtual reality that will significantly reduce the limitations of the mechanical articulator, and by simulation of real patient data, allow analyses with regard to static and dynamic occlusion as well as to jaw relation. It is the purpose of this article to present the concepts and strategies for a future replacement of the mechanical articulator by a virtual one. Also, a brief note on virtual reality haptic system has been highlighted along with newly developed touch enabled virtual articulator. PMID:25177664

  6. Prosthetic fitting after rotationplasty of the knee.

    Science.gov (United States)

    So, Noel F; Andrews, Karen L; Anderson, Kimberly; Gozola, Michael A; Shives, Thomas C; Rose, Peter S; Shaughnessy, William J; Sim, Franklin H

    2014-04-01

    The objectives of this study were to describe the authors' experience with the timeline for prosthetic fitting after rotationplasty of the knee and to determine factors that may be associated with earlier prosthetic fitting. The authors conducted a retrospective observational study of 12 patients who underwent rotationplasty of the knee and received prosthetic care at this institution. All patients had oncologic causes for surgery. The median age at amputation was 10 yrs. The overall survival rate was 92%. Five patients received a preliminary bypass prosthesis. All 12 patients were successfully fitted with a definitive prosthesis. Three patients were fitted within 90 days; two of these three patients did not require chemotherapy. The median time for definitive prosthetic fitting in the ten patients requiring chemotherapy was 230.5 days (range, 85-425 days). Nine patients had documentation supporting a return to sport/premorbid physical recreational activities. In the authors' experience, chemotherapy was associated with delayed definitive prosthetic fitting. Typically, the patients who required rotationplasty for cancer completed fitting with a definitive prosthesis in 6 mos. The findings of this study validate previous reports and confirm that most rotationplasty patients have excellent outcomes with return to premorbid physical activities.

  7. An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

    Energy Technology Data Exchange (ETDEWEB)

    Mieloszyk, Magdalena; Skarbek, Lukasz; Ostachowicz, Wieslaw [IFFM PASci, Fiszera14, 80-952 Gdansk (Poland); Krawczuk, Marek, E-mail: mmieloszyk@imp.gda.pl [IFFM PASci, Fiszera 14, 80-952 Gdansk and Technical University of Gdansk, Wlasna Strzecha 18a Street, 80-233, Gdansk (Poland)

    2011-07-19

    This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS (registered) and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

  8. Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

    Science.gov (United States)

    Cui, Yiqian; Shi, Junyou; Wang, Zili

    2015-11-01

    Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. A simulation study for the application of two different neural network control algorithms on an electrohydraulic system

    Science.gov (United States)

    İstif, İlyas

    2005-11-01

    This paper studies a servo-valve controlled hydraulic cylinder system which is mostly used in industrial applications such as robotics, computer numerical control (CNC) machines and transportations. The system model consists of combination of two models: The first model involves nonlinear flow equations of the servo-valve, which are widely available in the literature. The second model employed in the system is a tailored asymmetric cylinder model. A fourth order nonlinear system model is then obtained by combining these two models. Two different neural network control algorithms are applied to the system. The first algorithm is "Neural Network Predictive Control (NNPC)," which employs identified neural network model to predict the future output of the system. The second algorithm is "Nonlinear Autoregressive Moving Average (NARMA-L2)" control, which transforms nonlinear system dynamics into linear system dynamics by eliminating the nonlinearities. On the simulation, NNPC and NARMA-L2 control are applied to the system model by using Matlab's Simulik simulation package and position control of the system is realized. A discussion regarding the advantages and disadvantages of the two control algorithms are also provided in the paper.

  10. Cellular Neural Networks: A genetic algorithm for parameters optimization in artificial vision applications

    Energy Technology Data Exchange (ETDEWEB)

    Taraglio, S. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dipt. Innovazione; Zanela, A. [Rome Univ. `La Sapienza` (Italy). Dipt. di Fisica

    1997-03-01

    An optimization method for some of the CNN`s (Cellular Neural Network) parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN`s literature.

  11. A New Training Method for Analyzable Structured Neural Network and Application of Daily Peak Load Forecasting

    Science.gov (United States)

    Iizaka, Tatsuya; Matsui, Tetsuro; Fukuyama, Yoshikazu

    This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

  12. Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

    Science.gov (United States)

    Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

    2017-02-01

    The soil sorption partition coefficient logKoc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logKoc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logKoc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Application of analytical learning to the syntesis of neural network for process control of physical rehabilitation

    OpenAIRE

    Alexander Trunov

    2015-01-01

    The problem of analytical learning of artificial neural network (ANN) is consider. Solutions in the analytic form for synaptic weight coefficients (SWC) as recurrent sequence are obtained. Convergence of recurrent approximation for two scheme of approach by a linear and quadratic curve are proved and discussed

  14. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    Science.gov (United States)

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  15. Application of a neural network as a potential aid in predicting NTF pump failure

    Science.gov (United States)

    Rogers, James L.; Hill, Jeffrey S.; Lamarsh, William J., II; Bradley, David E.

    1993-01-01

    The National Transonic Facility has three centrifugal multi-stage pumps to supply liquid nitrogen to the wind tunnel. Pump reliability is critical to facility operation and test capability. A highly desirable goal is to be able to detect a pump rotating component problem as early as possible during normal operation and avoid serious damage to other pump components. If a problem is detected before serious damage occurs, the repair cost and downtime could be reduced significantly. A neural network-based tool was developed for monitoring pump performance and aiding in predicting pump failure. Once trained, neural networks can rapidly process many combinations of input values other than those used for training to approximate previously unknown output values. This neural network was applied to establish relationships among the critical frequencies and aid in predicting failures. Training pairs were developed from frequency scans from typical tunnel operations. After training, various combinations of critical pump frequencies were propagated through the neural network. The approximated output was used to create a contour plot depicting the relationships of the input frequencies to the output pump frequency.

  16. Application of Artificial Neural Networks for Efficient High-Resolution 2D DOA Estimation

    Directory of Open Access Journals (Sweden)

    M. Agatonović

    2012-12-01

    Full Text Available A novel method to provide high-resolution Two-Dimensional Direction of Arrival (2D DOA estimation employing Artificial Neural Networks (ANNs is presented in this paper. The observed space is divided into azimuth and elevation sectors. Multilayer Perceptron (MLP neural networks are employed to detect the presence of a source in a sector while Radial Basis Function (RBF neural networks are utilized for DOA estimation. It is shown that a number of appropriately trained neural networks can be successfully used for the high-resolution DOA estimation of narrowband sources in both azimuth and elevation. The training time of each smaller network is significantly re¬duced as different training sets are used for networks in detection and estimation stage. By avoiding the spectral search, the proposed method is suitable for real-time ap¬plications as it provides DOA estimates in a matter of seconds. At the same time, it demonstrates the accuracy comparable to that of the super-resolution 2D MUSIC algorithm.

  17. Utilising reinforcement learning to develop strategies for driving auditory neural implants

    Science.gov (United States)

    Lee, Geoffrey W.; Zambetta, Fabio; Li, Xiaodong; Paolini, Antonio G.

    2016-08-01

    Objective. In this paper we propose a novel application of reinforcement learning to the area of auditory neural stimulation. We aim to develop a simulation environment which is based off real neurological responses to auditory and electrical stimulation in the cochlear nucleus (CN) and inferior colliculus (IC) of an animal model. Using this simulator we implement closed loop reinforcement learning algorithms to determine which methods are most effective at learning effective acoustic neural stimulation strategies. Approach. By recording a comprehensive set of acoustic frequency presentations and neural responses from a set of animals we created a large database of neural responses to acoustic stimulation. Extensive electrical stimulation in the CN and the recording of neural responses in the IC provides a mapping of how the auditory system responds to electrical stimuli. The combined dataset is used as the foundation for the simulator, which is used to implement and test learning algorithms. Main results. Reinforcement learning, utilising a modified n-Armed Bandit solution, is implemented to demonstrate the model’s function. We show the ability to effectively learn stimulation patterns which mimic the cochlea’s ability to covert acoustic frequencies to neural activity. Time taken to learn effective replication using neural stimulation takes less than 20 min under continuous testing. Significance. These results show the utility of reinforcement learning in the field of neural stimulation. These results can be coupled with existing sound processing technologies to develop new auditory prosthetics that are adaptable to the recipients current auditory pathway. The same process can theoretically be abstracted to other sensory and motor systems to develop similar electrical replication of neural signals.

  18. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process.

    Science.gov (United States)

    Witek-Krowiak, Anna; Chojnacka, Katarzyna; Podstawczyk, Daria; Dawiec, Anna; Pokomeda, Karol

    2014-05-01

    A review on the application of response surface methodology (RSM) and artificial neural networks (ANN) in biosorption modelling and optimization is presented. The theoretical background of the discussed methods with the application procedure is explained. The paper describes most frequently used experimental designs, concerning their limitations and typical applications. The paper also presents ways to determine the accuracy and the significance of model fitting for both methodologies described herein. Furthermore, recent references on biosorption modelling and optimization with the use of RSM and the ANN approach are shown. Special attention was paid to the selection of factors and responses, as well as to statistical analysis of the modelling results. Copyright © 2014 Elsevier Ltd. All rights reserved.

  19. Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange

    Science.gov (United States)

    Zahedi, Javad; Rounaghi, Mohammad Mahdi

    2015-11-01

    Stock price changes are receiving the increasing attention of investors, especially those who have long-term aims. The present study intends to assess the predictability of prices on Tehran Stock Exchange through the application of artificial neural network models and principal component analysis method and using 20 accounting variables. Finally, goodness of fit for principal component analysis has been determined through real values, and the effective factors in Tehran Stock Exchange prices have been accurately predicted and modeled in the form of a new pattern consisting of all variables.

  20. Bruxism and prosthetic treatment: a critical review.

    Science.gov (United States)

    Johansson, Anders; Omar, Ridwaan; Carlsson, Gunnar E

    2011-07-01

    Based on the findings from available research on bruxism and prosthetic treatment published in the dental literature, an attempt was made to draw conclusions about the existence of a possible relationship between the two, and its clinical relevance. MEDLINE/PubMed searches were conducted using the terms 'bruxism' and 'prosthetic treatment', as well as combinations of these and related terms. The few studies judged to be relevant were critically reviewed, in addition to papers found during an additional manual search of reference lists within selected articles. Bruxism is a common parafunctional habit, occurring both during sleep and wakefulness. Usually it causes few serious effects, but can do so in some patients. The etiology is multifactorial. There is no known treatment to stop bruxism, including prosthetic treatment. The role of bruxism in the process of tooth wear is unclear, but it is not considered a major cause. As informed by the present critical review, the relationship between bruxism and prosthetic treatment is one that relates mainly to the effect of the former on the latter. Bruxism may be included among the risk factors, and is associated with increased mechanical and/or technical complications in prosthodontic rehabilitation, although it seems not to affect implant survival. When prosthetic intervention is indicated in a patient with bruxism, efforts should be made to reduce the effects of likely heavy occlusal loading on all the components that contribute to prosthetic structural integrity. Failure to do so may indicate earlier failure than is the norm. Copyright © 2011 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

  1. Application of Chaos Theory and Artificial Neural Networks to Evaluate Evaporation from Lake's Water Surface

    Directory of Open Access Journals (Sweden)

    Saeed Farzin

    2017-06-01

    Full Text Available Introduction: Dynamic nature of hydrological phenomena and the limited availability of appropriate mathematical tools caused the most previous studies in this field led to the random and the probabilistic approach. So selection the best model for evaluation of these phenomena is essential and complex. Nowadays different models are used for evaluation and prediction of hydrological phenomena. Damle and Yalcin (2007 estimated river runoff by chaos theory. khatibi et al (2012 used artificial neural network and gene expression programming to predict relative humidity. Zounemat and Kisi (2015 evaluated chaotic behavior of marine wind-wave system of Caspian sea. One of the important hydrological phenomena is evaporation, especially in lakes. The investigation of deterministic and stochastic behavior of water evaporation values in the lakes in order to select the best simulation approach and capable of prediction is an important and controversial issue that has been studied in this research. Materials and Methods: In the present paper, monthly values of evaporation are evaluated by two different models. Chaos theory and artificial neural network are used for the analysis of stochastic behavior and capability of prediction of water evaporation values in the Urmia Lake in northwestern of Iran. In recent years, Urmia Lake has unpleasant changes and drop in water level due to inappropriate management and climate change. One of the important factors related to climate change, is evaporation. Urmia Lake is a salt lake, and because of existence valuable ecology, environmental issues and maintenance of ecosystems of this lake are very important. So evaporation can have an essential role in the salinity, environmental and the hydrological cycle of the lake. In this regard, according to the ability of chaos theory and artificial neural network to analysis nonlinear dynamic systems; monthly values of evaporation, during a 40-year period, are investigated and then

  2. Optimising the prescription of prosthetic technologies (opptec): Outcome measures for evidence based prosthetic practice and use

    LENUS (Irish Health Repository)

    Ryall, Dr Nicola

    2010-01-01

    This study provided a forum for patients and service providers to voice their opinions in what they believe to be the important predictors and outcomes involved in successful rehabilitation following limb loss. To develop a consensus on the most important outcomes and factors to address for both the lower limb and upper limb prosthetic prescription process, the above data relating to lower limb and upper prosthetics were subsequently used in the next phase of the research involving two Delphi surveys of 23 and 53 experts within the lower limb and upper limb amputation and prosthetic field respectively, including users, service providers and researchers.\\r\

  3. Mesofluidic controlled robotic or prosthetic finger

    Science.gov (United States)

    Lind, Randall F; Jansen, John F; Love, Lonnie J

    2013-11-19

    A mesofluidic powered robotic and/or prosthetic finger joint includes a first finger section having at least one mesofluidic actuator in fluid communication with a first actuator, a second mesofluidic actuator in fluid communication with a second actuator and a second prosthetic finger section pivotally connected to the first finger section by a joint pivot, wherein the first actuator pivotally cooperates with the second finger to provide a first mechanical advantage relative to the joint point and wherein the second actuator pivotally cooperates with the second finger section to provide a second mechanical advantage relative to the joint point.

  4. [Prosthetic rehabilitation: needs in Senegalese dental offices].

    Science.gov (United States)

    Mbodj, E B; Diouf, M; Faye, D; Ndiaye, A; Seck, M T; Ndiaye, C; Diallo, P D

    2011-12-01

    Knowledge of dental prosthetic needs will develop strategies for prevention and treatment through a package of individual, community and professional policies. The aim of this study was to evaluate prosthetic needs in Senegalese dental offices. The survey was conducted among people aged 15 years and more attending Senegalese dental clinics. The mean number of missing teeth was 4.4. Only 55.3% of the sample expressed the need for dentures and 81.8% had a diagnosed need for prosthesis. A statistically significant difference was noticed between the needs diagnosed and the expressed needs (p dental offices.

  5. Neural network model for growth of Salmonella serotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment

    Science.gov (United States)

    With the advent of commercial software applications, it is now easy to develop neural network models for predictive microbiology applications. However, different versions of the model may be required to meet the divergent needs of model users. In the current study, the commercial software applicat...

  6. Comparison of the applicability of neural networks and cluster classification methods on the example company's financial situation

    Directory of Open Access Journals (Sweden)

    Oldřich Trenz

    2010-01-01

    Full Text Available The paper is focused on comparing the classification ability of the model with self-learning neutral network and methods from cluster analysis. The emphasis is particularly on the comparison of different approaches to a specific application example of the commitment, the classification of then financial situation. The aim is to critically evaluate different approaches at the level of application and deployment options.The verify the classification capability of the different approaches were used financial data from the database „Credit Info“, in particular data describing the financial situation of the two hundred eleven farms of homogeneous and uniform primary field.Input data were from the methods used, modified and evaluated by appropriate methodology. Found the final solution showed that the used approaches do not show significant differences, and they can say that they are equivalent. Based on this finding can formulate the conclusion that the approach of artificial intelligence (self-learning neural network is as effective as a partial methods in the field of cluster analysis. In both cases, these approaches can be an invaluable tool in decision making.When the financial situation is evaluated by the expert, the calculation of liquidity, profitability and other financial indicators are making some simplification. In this respect, neural networks perform better, since these simplifications in them selves are not natively included. They can better assess and somewhat ambiguous cases, including businesses with undefined financial situation, the so-called data in the border region. In this respect, support and representation of the graphical layout of the resulting situation sorted out objects using software implemented neural network model.

  7. Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting

    Directory of Open Access Journals (Sweden)

    Jianjin Wang

    2017-01-01

    Full Text Available Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper and knowledge-based method (traditional hydrological model may booster simulation accuracy. In this study, we proposed a new back-propagation (BP neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

  8. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  9. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  10. Artificial neural networks in variable process control: application in particleboard manufacture

    Energy Technology Data Exchange (ETDEWEB)

    Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.

    2009-07-01

    Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.

  11. Constraints of Biological Neural Networks and Their Consideration in AI Applications

    Directory of Open Access Journals (Sweden)

    Richard Stafford

    2010-01-01

    Full Text Available Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances. Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

  12. A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

    Directory of Open Access Journals (Sweden)

    P.-Y. Chen

    2009-01-01

    Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

  13. REDES NEURAIS, LÓGICA NEBULOSA E ALGORITMOS GENÉTICOS: APLICAÇÕES E POSSIBILIDADES EM FINANÇAS E CONTABILIDADE/NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

    National Research Council Canada - National Science Library

    Artur Filipe Ewald Wuerges; José Alonso Borba

    2010-01-01

    .... This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to...

  14. 21 CFR 895.101 - Prosthetic hair fibers.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Prosthetic hair fibers. 895.101 Section 895.101...) MEDICAL DEVICES BANNED DEVICES Listing of Banned Devices § 895.101 Prosthetic hair fibers. Prosthetic hair fibers are devices intended for implantation into the human scalp to simulate natural hair or conceal...

  15. Comparative roll-over analysis of prosthetic feet

    NARCIS (Netherlands)

    Curtze, Carolin; Hof, At L.; van Keeken, Helco G.; Halbertsma, Jan P. K.; Postema, Klaas; Otten, Bert

    2009-01-01

    A prosthetic foot is a key element of a prosthetic leg, literally forming the basis for a stable and efficient amputee gait. We determined the roll-over characteristics of a broad range of prosthetic feet and examined the effect of a variety of shoes on these characteristics. The body weight of a

  16. A Neural Network MLSE Receiver Based on Natural Gradient Descent: Application to Satellite Communications

    Directory of Open Access Journals (Sweden)

    Ibnkahla Mohamed

    2004-01-01

    Full Text Available The paper proposes a maximum likelihood sequence estimator (MLSE receiver for satellite communications. The satellite channel model is composed of a nonlinear traveling wave tube (TWT amplifier followed by a multipath propagation channel. The receiver is composed of a neural network channel estimator (NNCE and a Viterbi detector. The natural gradient (NG descent is used for training. Computer simulations show that the performance of our receiver is close to the ideal MLSE receiver in which the channel is perfectly known.

  17. Application of Artificial Neural Networks in Modeling Direction Wheelchairs Using Neurosky Mindset Mobile (EEG Device

    Directory of Open Access Journals (Sweden)

    Agus Siswoyo

    2017-07-01

    Full Text Available The implementation of Artificial Neural Network in prediction the direction of electric wheelchair from brain signal input for physical mobility impairment.. The control of the wheelchair as an effort in improving disabled person life quality. The interaction from disabled person is helping in relation to social life with others. Because of the mobility impairment, the wheelchair with brain signal input is made. This wheel chair is purposed to help the disabled person and elderly for their daily activity. ANN helps to develop the mapping from input to target. ANN is developed in 3 level: input level, one hidden level, and output level (6-2-1. There are 6 signal from Neurosky Mindset sensor output, Alpha1, Alpha2, Raw signal, Total time signal, Attention Signal, and Meditation signal. The purpose of this research is to find out the output value from ANN: value in turning right, turning left, and forward. From those outputs, we can prove the relevance to the target. One of the main problem that interfering with success is the problem of proper neural network training. Arduino uno is chosen to implement the learning program algorithm because it is a popular microcontroller that is economic and efficient. The training of artificial neural network in this research uses 21 data package from raw data, Alpha1, Aplha2, Meditation data, Attention data, total time data. At the time of the test there is a value of Mean square Error(MSE at the end of training amounted to 0.92495 at epoch 9958, value a correlation coefficient of 0.92804 shows that accuracy the results of the training process good.     Keywords: Navigation, Neural network, Real-time training, Arduino

  18. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    OpenAIRE

    Lucas Antón Pastur-Romay; Francisco Cedrón; Alejandro Pazos; Ana Belén Porto-Pazos

    2016-01-01

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by D...

  19. A Neural network model for the separation of atmospheric effects on attenuation: Application to frequency scaling.

    OpenAIRE

    Barthès, Laurent; Mallet, Cécile; Brisseau, O.

    2006-01-01

    Attenuation due to the propagation of radio waves through the Earth's atmosphere plays a major role in satellite link attenuation at frequencies beyond 20 GHz. This paper presents the development of an artificial neural network (ANN) to separate out the respective roles played by the three types of contributor, namely, gases (oxygen and water vapor), clouds, and rain, to the overall attenuation of radio waves. Whereas the inputs to the ANN are the total attenuation measured at either one, two...

  20. Design and evaluation of two different finger concepts for body-powered prosthetic hand

    NARCIS (Netherlands)

    Smit, G.; Plettenburg, D.H.; Van der Helm, F.C.

    2014-01-01

    The goal of this study was to find an efficient method of energy transmission for application in an anthropomorphic underactuated body-powered (BP) prosthetic hand. A pulley-cable finger and a hydraulic cylinder finger were designed and tested to compare the pulley-cable transmission principle with

  1. Using Neural Data to Test A Theory of Investor Behavior: An Application to Realization Utility.

    Science.gov (United States)

    Frydman, Cary; Barberis, Nicholas; Camerer, Colin; Bossaerts, Peter; Rangel, Antonio

    2014-04-01

    We use measures of neural activity provided by functional magnetic resonance imaging (fMRI) to test the "realization utility" theory of investor behavior, which posits that people derive utility directly from the act of realizing gains and losses. Subjects traded stocks in an experimental market while we measured their brain activity. We find that all subjects exhibit a strong disposition effect in their trading, even though it is suboptimal. Consistent with the realization utility explanation for this behavior, we find that activity in the ventromedial prefrontal cortex, an area known to encode the value of options during choices, correlates with the capital gains of potential trades; that the neural measures of realization utility correlate across subjects with their individual tendency to exhibit a disposition effect; and that activity in the ventral striatum, an area known to encode information about changes in the present value of experienced utility, exhibits a positive response when subjects realize capital gains. These results provide support for the realization utility model and, more generally, demonstrate how neural data can be helpful in testing models of investor behavior.

  2. A neural network application to search and rescue satellite aided tracking (SARSAT)

    Science.gov (United States)

    Taylor, Ivan W.; Vigneault, Michel O.

    1992-04-01

    The Search and Rescue Satellite Aided Tracking (SARSAT) system uses polar orbiting satellites to monitor the earth's surface for emergency locator transmitters (ELT's). A detected ELT signal is retransmitted to ground facilities for processing. The ELT location is determined by fitting a Doppler curve to the signal, and this location estimate is then sent for transmission to a rescue unit for action. The heuristic based approach to predict the location accuracy category of SARSAT hits based on the parameters of the Doppler curve fit work reasonably well but are far from ideal. They consistently have difficulty identifying very good solutions when they occur and very poor solutions when they occur. Normal data fitting techniques based on multiple linear regression do not work well because SARSAT data are inherently nonlinear. Neural networks were thus applied to SARSAT data as a nonlinear data fitting technique. A neural network program was trained on a sample of 173 observations and tested on a sample of 172 observations. Results are compared to the current method. Although the results so far have been promising, they are still far from ideal. Other artificial intelligence technologies such as fuzzy logic, expert systems, and inductive learning are being investigated to enhance the neural network approach.

  3. Localization and Recovery of Peripheral Neural Sources with Beamforming Algorithms

    Science.gov (United States)

    Wodlinger, Brian; Durand, Dominique M.

    2013-01-01

    The peripheral nervous system carries sensory and motor information that could be useful as command signals for function restoration in areas such as neural prosthetics and Functional Electrical Stimulation (FES). Nerve cuff electrodes provide a robust and safe technique for recording nerve signals. However, a method to separate and recover signals from individual fascicles is necessary. Prior knowledge of the electrode geometry was used to develop an algorithm which assumes neither signal independence nor detailed knowledge of the nerve’s geometry/conductivity, and is applicable to any wide-band near-field situation. When used to recover fascicular activities from simulated nerve cuff recordings in a realistic human femoral nerve model, this beamforming algorithm separates signals as close as 1.5mm with cross-correlation coefficient, R>0.9 (10% noise). Ten simultaneous signals could be recovered from individual fascicles with only a 20% decrease in R compared to a single signal. At high noise levels (40%), sources were localized to 180±170 μm in the 12x3mm cuff. Localizing sources and using the resulting positions in the recovery algorithm yielded R=0.66±0.10 in 10% noise for 5 simultaneous muscle-activation signals from synergistic fascicles. These recovered signals should allow natural, robust, closed-loop control of multiple degree-of-freedom prosthetic devices and FES systems. PMID:19840913

  4. Parallelization of learning problems by artificial neural networks. Application in external radiotherapy; Parallelisation de problemes d'apprentissage par des reseaux neuronaux artificiels. Application en radiotherapie externe

    Energy Technology Data Exchange (ETDEWEB)

    Sauget, M

    2007-12-15

    This research is about the application of neural networks used in the external radiotherapy domain. The goal is to elaborate a new evaluating system for the radiation dose distributions in heterogeneous environments. The al objective of this work is to build a complete tool kit to evaluate the optimal treatment planning. My st research point is about the conception of an incremental learning algorithm. The interest of my work is to combine different optimizations specialized in the function interpolation and to propose a new algorithm allowing to change the neural network architecture during the learning phase. This algorithm allows to minimise the al size of the neural network while keeping a good accuracy. The second part of my research is to parallelize the previous incremental learning algorithm. The goal of that work is to increase the speed of the learning step as well as the size of the learned dataset needed in a clinical case. For that, our incremental learning algorithm presents an original data decomposition with overlapping, together with a fault tolerance mechanism. My last research point is about a fast and accurate algorithm computing the radiation dose deposit in any heterogeneous environment. At the present time, the existing solutions used are not optimal. The fast solution are not accurate and do not give an optimal treatment planning. On the other hand, the accurate solutions are far too slow to be used in a clinical context. Our algorithm answers to this problem by bringing rapidity and accuracy. The concept is to use a neural network adequately learned together with a mechanism taking into account the environment changes. The advantages of this algorithm is to avoid the use of a complex physical code while keeping a good accuracy and reasonable computation times. (author)

  5. Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

    Science.gov (United States)

    Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

    2016-10-01

    Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

  6. Consumer satisfaction in prosthetics and orthotics facilities

    NARCIS (Netherlands)

    Geertzen, J.H.B.; Gankema, H.G.J.; Groothoff, J.W.; Dijkstra, P.U.

    The aim of this study was to assess consumer/patient satisfaction with the services of the prosthetics and orthotics (P&O) facilities in the north of the Netherlands, using a modified SERVQUAL questionnaire. In this questionnaire, consumer interests and experiences are assessed on a 5-point Likert

  7. Successful Thrombolysis of Aortic Prosthetic Valve Thrombosis ...

    African Journals Online (AJOL)

    Arun Kumar Agnihotri

    She delivered a normal baby uneventfully in follow up at full term of pregnancy with no complications. Fibrinolytic therapy for mechanical valve thrombosis is a reasonable alternative to surgery in first trimester of pregnancy. KEY WORDS: Prosthetic valve thrombosis; Echocardiography; Streptokinase;. Thrombolysis; Fetus.

  8. Prosthetic Management of Patients Presenting with Juvenile ...

    African Journals Online (AJOL)

    Conclusion: The main objective of prosthetic rehabilitation is the restoration of the integrity of the dental arches. However, the choice of teeth replaced in the partial dentures provided for the patients in this study was based on the choice of the patients, and their choice was determined by aesthetics and affordability.

  9. Promoting prosthetics in the Latino community.

    Science.gov (United States)

    Jaramillo, R; Barabe, J G; Cupp, D

    1995-01-01

    This article describes an effective approach to informing the Latino community about prosthetics. Unlike English, little information on this subject is available in Spanish. The process of obtaining, fabricating, and wearing a prosthesis was interwoven into the teleplay "Milagros." The story concept, video production, and the Latino population's cultural characteristics are discussed. The audience welcomed the opportunity to share the information with others.

  10. Pregnancy after Prosthetic Aortic Valve Replacement: How Do We Monitor Prosthetic Valvular Function during Pregnancy?

    Directory of Open Access Journals (Sweden)

    Nicole Sahasrabudhe

    2018-01-01

    Full Text Available Background. With modern medicine, many women after structural heart repair are deciding to experience pregnancy. There is a need for further study to identify normal echocardiographic parameters to better assess prosthetic valvular function in pregnancy. In addition, a multidisciplinary approach is essential in managing pregnant patients with complex cardiac conditions. Case. A 22-year-old nulliparous woman with an aortic valve replacement 18 months prior to her pregnancy presented to prenatal care at 20-week gestation. During her prenatal care, serial echocardiography showed a significant increase in the mean gradient across the prosthetic aortic valve. Multidisciplinary management and a serial echocardiography played an integral role in her care that resulted in a successful spontaneous vaginal delivery without complications. Conclusion. Further characterization of the normal echocardiographic parameters in pregnant patients with prosthetic valves is critical to optimize prenatal care for this patient population. This case report is novel in that serial echocardiograms were obtained throughout prenatal care, which showed significant changes across the prosthetic aortic valve. Teaching Points. (1 Further study is needed to identify normal echocardiographic parameters to best assess prosthetic valvular function in pregnancy. (2 Multidisciplinary management is encouraged to optimize prenatal care for women with prosthetic aortic valve replacements.

  11. Long time series of soil moisture obtained using neural networks: application to AMSR-E and SMOS

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio J.; Kerr, Yann H.; de Jeu, Rcihard A. M.; van der Schalie, Robin; Wigneron, Jean Pierre; Ayaari, Amen al; Dolman, Han; Drusch, Matthias; Mecklenburg, Sussane

    2015-04-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure soil moisture (hereafter SM) from space. The instrument on-board SMOS is a L-band aperture synthesis radiometer, with full-polarization and multi-angular capabilities (Mecklenburg et al. 2012). The operational SM retrieval algorithm is based on a physical model (Kerr et al. 2012). In addition, Rodriguez-Fernandez et al. (2014) have recently implemented an inverse model based in neural networks using the approach of Aires & Prigent (2006), which consists in training the neural networks with numerical weather prediction models (ECMWF, Balsamo et al. 2009). In the context of an ESA funded project (de Jeu et al, this conference, session CL 5.7), we have studied this neural network approach to create a consistent soil moisture dataset from 2003 to 2014 using NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E) and ESA SMOS radiometers as input data. Two neural networks algorithms have been defined and optimized using AMSR-E or SMOS as input data in the periods 2003-Oct 2011 and 2010-2014, respectively. The two missions overlapping period has been used to demonstrate the consistency of the SM dataset produced with both algorithms by comparing monthly averages of SM and by comparing with time series of in situ measurements at selected locations and other SM products such as the SMOS operational SM, ECMWF model SM, and AMSR-E LPRM SM (Owe et al. 2008). Finally, the long time series of SM obtained with neural networks will be compared to in-situ measurements and ECMWF ERA-Interim SM at selected locations. This long-term soil moisture dataset can be used for hydrological and climate applications and it is the first step towards a longer dataset which will include additional sensors. References Aires, F. & Prigent, C. Toward a new generation of satellite surface products? Journal of Geophysical Research: Atmospheres (1984--2012), Wiley Online Library, 2006, 11

  12. Comparative roll-over analysis of prosthetic feet.

    Science.gov (United States)

    Curtze, Carolin; Hof, At L; van Keeken, Helco G; Halbertsma, Jan P K; Postema, Klaas; Otten, Bert

    2009-08-07

    A prosthetic foot is a key element of a prosthetic leg, literally forming the basis for a stable and efficient amputee gait. We determined the roll-over characteristics of a broad range of prosthetic feet and examined the effect of a variety of shoes on these characteristics. The body weight of a person acting on a prosthetic foot during roll-over was emulated by means of an inverted pendulum-like apparatus. Parameters measured were the effective radius of curvature, the forward travel of the center of pressure, and the instantaneous radius of curvature of the prosthetic feet. Finally, we discuss how these parameters relate to amputee gait.

  13. Application of Artificial Neural Networks and Regression for Analysis Of Chemical Steel Reheating

    Directory of Open Access Journals (Sweden)

    Jan MORÁVKA

    2009-06-01

    Full Text Available Metallurgical processes belong to complex physical-chemical processes theoretically described by means of multidimensional generally nonlinear dynamic systems with different transfer lags in their structure. Before realization of these systems control requested by practice it is necessary to execute their structural and parametric identification. As these processes are very complex, all exact relations for their mathematical description are not known so far. Some metallurgical systems are practically non-described so far (black box, further described only partially (grey box, while only a little of them are described almost fully (white box. Determination of internal structure of insufficiently described systems is done by means physical modelling, by measurement if important data and subsequently by means of regression analysis or artificial neural networks applied to measured data. There is certain chance to determine a proper system internal structure at system identification by means of statistical analysis (i.e. to come from black box to grey box or from grey box to white box, though this approach is knowledge and time-consuming. Identification by means of artificial neural networks enables rather external system description (i.e. black box models creation, when we get an acceptable accordance between real and modelled outputs, i.e. so called output estimation (prediction. This approach is thus more suitable for control than for identification itself. Contribution deals with a possibility of prediction of a temperature after a steel chemical heating on device of integrated system of secondary metallurgy by means of regression analysis and artificial neural networks and with a comparison of both of these approaches.

  14. Application of regression and neural models to predict competitive swimming performance.

    Science.gov (United States)

    Maszczyk, Adam; Roczniok, Robert; Waśkiewicz, Zbigniew; Czuba, Miłosz; Mikołajec, Kazimierz; Zajac, Adam; Stanula, Arkadiusz

    2012-04-01

    This research problem was indirectly but closely connected with the optimization of an athlete-selection process, based on predictions viewed as determinants of future successes. The research project involved a group of 249 competitive swimmers (age 12 yr., SD = 0.5) who trained and competed for four years. Measures involving fitness (e.g., lung capacity), strength (e.g., standing long jump), swimming technique (turn, glide, distance per stroke cycle), anthropometric variables (e.g., hand and foot size), as well as specific swimming measures (speeds in particular distances), were used. The participants (n = 189) trained from May 2008 to May 2009, which involved five days of swimming workouts per week, and three additional 45-min. sessions devoted to measurements necessary for this study. In June 2009, data from two groups of 30 swimmers each (n = 60) were used to identify predictor variables. Models were then constructed from these variables to predict final swimming performance in the 50 meter and 800 meter crawl events. Nonlinear regression models and neural models were built for the dependent variable of sport results (performance at 50m and 800m). In May 2010, the swimmers' actual race times for these events were compared to the predictions created a year prior to the beginning of the experiment. Results for the nonlinear regression models and perceptron networks structured as 8-4-1 and 4-3-1 indicated that the neural models overall more accurately predicted final swimming performance from initial training, strength, fitness, and body measurements. Differences in the sum of absolute error values were 4:11.96 (n = 30 for 800m) and 20.39 (n = 30 for 50m), for models structured as 8-4-1 and 4-3-1, respectively, with the neural models being more accurate. It seems possible that such models can be used to predict future performance, as well as in the process of recruiting athletes for specific styles and distances in swimming.

  15. Fast inline inspection by Neural Network Based Filtered Backprojection: Application to apple inspection

    Directory of Open Access Journals (Sweden)

    Eline Janssens

    2016-11-01

    Full Text Available Speed is an important parameter of an inspection system. Inline computed tomography systems exist but are generally expensive. Moreover, their throughput is limited by the speed of the reconstruction algorithm. In this work, we propose a Neural Network-based Hilbert transform Filtered Backprojection (NN-hFBP method to reconstruct objects in an inline scanning environment in a fast and accurate way. Experiments based on apple X-ray scans show that the NN-hFBP method allows to reconstruct images with a substantially better tradeoff between image quality and reconstruction time.

  16. Application of neural network and Levenberg–Marquardt algorithm for electrical grid planning

    Science.gov (United States)

    Boopathi, M.; Senthil Kumar, S.; Kalaiarassan, G.

    2017-11-01

    In the present scenario of industrial growth, technological advancement and population growth, the single most inseparable commodity is electrical energy. This paper presents a novel way to use Neural Networks to forecast long-term electricity load and use the result for proper grid planning in terms of expansion and maintenance. Uninterrupted, reliable and cheap electricity can only be available if there is a proper planned and stable grid, which can be achieved only through proper futuristic grid planning models. In this paper, focus is centered to form the cluster of areas based on the forecasted energy needs in India. Priority ranking also allocated to decide the level of expansion for forthcoming decades.

  17. Application of an artificial neural network for evaluation of activity concentration exemption limits in NORM industry.

    Science.gov (United States)

    Wiedner, Hannah; Peyrés, Virginia; Crespo, Teresa; Mejuto, Marcos; García-Toraño, Eduardo; Maringer, Franz Josef

    2017-08-01

    NORM emits many different gamma energies that have to be analysed by an expert. Alternatively, artificial neural networks (ANNs) can be used. These mathematical software tools can generalize "knowledge" gained from training datasets, applying it to new problems. No expert knowledge of gamma-ray spectrometry is needed by the end-user. In this work an ANN was created that is able to decide from the raw gamma-ray spectrum if the activity concentrations in a sample are above or below the exemption limits. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate

    Directory of Open Access Journals (Sweden)

    Mach Łukasz

    2017-06-01

    Full Text Available The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.

  19. An FPGA hardware/software co-design towards evolvable spiking neural networks for robotics application.

    Science.gov (United States)

    Johnston, S P; Prasad, G; Maguire, L; McGinnity, T M

    2010-12-01

    This paper presents an approach that permits the effective hardware realization of a novel Evolvable Spiking Neural Network (ESNN) paradigm on Field Programmable Gate Arrays (FPGAs). The ESNN possesses a hybrid learning algorithm that consists of a Spike Timing Dependent Plasticity (STDP) mechanism fused with a Genetic Algorithm (GA). The design and implementation direction utilizes the latest advancements in FPGA technology to provide a partitioned hardware/software co-design solution. The approach achieves the maximum FPGA flexibility obtainable for the ESNN paradigm. The algorithm was applied as an embedded intelligent system robotic controller to solve an autonomous navigation and obstacle avoidance problem.

  20. The application of a neural network methodology to the analysis of a dyeing operation

    Energy Technology Data Exchange (ETDEWEB)

    Hench, K.W.; Al-Ghanim, A.M. [New Mexico State Univ., Las Cruces, NM (United States). Dept. of Industrial Engineering

    1995-09-01

    The purpose of a dyeing process is to impart into a fiber a color that has desirable qualities through the use of a bath solution. The success of an operation is dependent on a variety of factors including fiber content, dye composition, dyebath pH, time, and temperature. In the event of a failed run, the addition of a correct amount of each dye that will move the dye run from a fail condition to a pass condition is a subjective judgement of an experienced operator. This paper presents a neural network approach for analyzing the dyeing process. Predictions of dye additions were obtained with promising results.

  1. Application of Polynomial Neural Networks to Classification of Acoustic Warfare Signals

    Science.gov (United States)

    1993-04-01

    fcf’𔄁IC nf20 uqe o.’ C eouinq thil b4𔃺*e to yWash’nOntq .1u0tos Svverl’ t. O’at r ;0, rl’ -f’ Of 0ly ,"non s *( O no 4’ W~c’mt 1)$ ’T .c~qOuQ~O ’ llno...Report NOSC TD 1855, Naval Ocean Systems Center, San Diego, May, 1990. [34] Ghosh, J., L. Deuser, and S. Beck, "A neural network based hybrid system

  2. Gapped sequence alignment using artificial neural networks: application to the MHC class I system

    DEFF Research Database (Denmark)

    Andreatta, Massimo; Nielsen, Morten

    2016-01-01

    . On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment. Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods...... the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm. Availability and implementation: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped...... sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0....

  3. Application of Neural Network Modeling to Identify Auditory Processing Disorders in School-Age Children

    Directory of Open Access Journals (Sweden)

    Sridhar Krishnamurti

    2015-01-01

    Full Text Available P300 Auditory Event-Related Potentials (P3AERPs were recorded in nine school-age children with auditory processing disorders and nine age- and gender-matched controls in response to tone burst stimuli presented at varying rates (1/second or 3/second under varying levels of competing noise (0 dB, 40 dB, or 60 dB SPL. Neural network modeling results indicated that speed of information processing and task-related demands significantly influenced P3AERP latency in children with auditory processing disorders. Competing noise and rapid stimulus rates influenced P3AERP amplitude in both groups.

  4. High-dimensional neural network potentials for multicomponent systems: First applications to zinc oxide

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Maschke, Marcus; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2010-07-01

    Recently, artificial neural networks (NN) trained to first-principles data have shown to provide accurate potential energy surfaces for systems containing a single atomic species. In this work we present an extension of the NN approach to multicomponent systems by introducing physically motivated terms to deal with long-range interactions. This is a necessary condition for studying binary systems and general multicomponent systems with significant charge transfer. The capabilities of the method are demonstrated for crystal structures, amorphous structures, clusters, and surfaces of zinc oxide as a benchmark system. We show that the predicted energies and forces are in excellent agreement with reference density-functional theory calculations.

  5. Prosthetics & Orthotics Manufacturing Initiative (POMI)

    Science.gov (United States)

    2012-12-21

    industr s program. stom braide carrier Wa design was f pinch poi cluded a pr he controls swing arm , e program’ practical giv he cost of d sarily...last two decades, the fact remains that if the socket does not fit comfortably, the wearer will reject the prosthesis regardless of the components...the gel liner used with their normal prosthesis . A waist belt (for transtibial applications) or shoulder strap (for transfemoral applications) was

  6. In vivo evaluation of a neural stem cell-seeded prosthesis

    Science.gov (United States)

    Purcell, E. K.; Seymour, J. P.; Yandamuri, S.; Kipke, D. R.

    2009-04-01

    Neural prosthetics capable of recording or stimulating neuronal activity may restore function for patients with motor and sensory deficits resulting from injury or degenerative disease. However, overcoming inconsistent recording quality and stability in chronic applications remains a significant challenge. A likely reason for this is the reactive tissue response to the devices following implantation into the brain, which is characterized by neuronal loss and glial encapsulation. We have developed a neural stem cell-seeded probe to facilitate integration of a synthetic prosthesis with the surrounding brain tissue. We fabricated parylene devices that include an open well seeded with neural stem cells encapsulated in an alginate hydrogel scaffold. Quantitative and qualitative data describing the distribution of neuronal, glial, and progenitor cells surrounding seeded and control devices are reported over four time points spanning 3 months. Neuronal loss and glial encapsulation associated with cell-seeded probes were mitigated during the initial week of implantation and exacerbated by 6 weeks post-insertion compared to control conditions. We hypothesize that graft cells secrete neuroprotective and neurotrophic factors that effect the desired healing response early in the study, with subsequent cell death and scaffold degradation accounting for a reversal of these results later. Applications of this biohybrid technology include future long-term neural recording and sensing studies.

  7. Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis

    Science.gov (United States)

    Eftekhari Zadeh, E.; Feghhi, S. A. H.; Roshani, G. H.; Rezaei, A.

    2016-05-01

    Due to variation of neutron energy spectrum in the target sample during the activation process and to peak overlapping caused by the Compton effect with gamma radiations emitted from activated elements, which results in background changes and consequently complex gamma spectrum during the measurement process, quantitative analysis will ultimately be problematic. Since there is no simple analytical correlation between peaks' counts with elements' concentrations, an artificial neural network for analyzing spectra can be a helpful tool. This work describes a study on the application of a neural network to determine the percentages of cement elements (mainly Ca, Si, Al, and Fe) using the neutron capture delayed gamma-ray spectra of the substance emitted by the activated nuclei as patterns which were simulated via the Monte Carlo N-particle transport code, version 2.7. The Radial Basis Function (RBF) network is developed with four specific peaks related to Ca, Si, Al and Fe, which were extracted as inputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model, several structures have been constructed and tested. The comparison between simulated and predicted values using the proposed RBF model shows that there is a good agreement between them.

  8. 3-D Bioprinting of Neural Tissue for Applications in Cell Therapy and Drug Screening

    Directory of Open Access Journals (Sweden)

    Michaela Thomas

    2017-11-01

    Full Text Available Neurodegenerative diseases affect millions of individuals in North America and cost the health-care industry billions of dollars for treatment. Current treatment options for degenerative diseases focus on physical rehabilitation or drug therapies, which temporarily mask the effects of cell damage, but quickly lose their efficacy. Cell therapies for the central nervous system remain an untapped market due to the complexity involved in growing neural tissues, controlling their differentiation, and protecting them from the hostile environment they meet upon implantation. Designing tissue constructs for the discovery of better drug treatments are also limited due to the resolution needed for an accurate cellular representation of the brain, in addition to being expensive and difficult to translate to biocompatible materials. 3-D printing offers a streamlined solution for engineering brain tissue for drug discovery or, in the future, for implantation. New microfluidic and bioplotting devices offer increased resolution, little impact on cell viability and have been tested with several bioink materials including fibrin, collagen, hyaluronic acid, poly(caprolactone, and poly(ethylene glycol. This review details current efforts at bioprinting neural tissue and highlights promising avenues for future work.

  9. 3-D Bioprinting of Neural Tissue for Applications in Cell Therapy and Drug Screening.

    Science.gov (United States)

    Thomas, Michaela; Willerth, Stephanie M

    2017-01-01

    Neurodegenerative diseases affect millions of individuals in North America and cost the health-care industry billions of dollars for treatment. Current treatment options for degenerative diseases focus on physical rehabilitation or drug therapies, which temporarily mask the effects of cell damage, but quickly lose their efficacy. Cell therapies for the central nervous system remain an untapped market due to the complexity involved in growing neural tissues, controlling their differentiation, and protecting them from the hostile environment they meet upon implantation. Designing tissue constructs for the discovery of better drug treatments are also limited due to the resolution needed for an accurate cellular representation of the brain, in addition to being expensive and difficult to translate to biocompatible materials. 3-D printing offers a streamlined solution for engineering brain tissue for drug discovery or, in the future, for implantation. New microfluidic and bioplotting devices offer increased resolution, little impact on cell viability and have been tested with several bioink materials including fibrin, collagen, hyaluronic acid, poly(caprolactone), and poly(ethylene glycol). This review details current efforts at bioprinting neural tissue and highlights promising avenues for future work.

  10. Wavelet neural networks initialization using hybridized clustering and harmony search algorithm: Application in epileptic seizure detection

    Science.gov (United States)

    Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline

    2013-04-01

    Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.

  11. Application of a two-stage fuzzy neural network to a prostate cancer prognosis system.

    Science.gov (United States)

    Kuo, Ren-Jieh; Huang, Man-Hsin; Cheng, Wei-Che; Lin, Chih-Chieh; Wu, Yung-Hung

    2015-02-01

    This study intends to develop a two-stage fuzzy neural network (FNN) for prognoses of prostate cancer. Due to the difficulty of making prognoses of prostate cancer, this study proposes a two-stage FNN for prediction. The initial membership function parameters of FNN are determined by cluster analysis. Then, an integration of the optimization version of an artificial immune network (Opt-aiNET) and a particle swarm optimization (PSO) algorithm is developed to investigate the relationship between the inputs and outputs. The evaluation results for three benchmark functions show that the proposed two-stage FNN has better performance than the other algorithms. In addition, model evaluation results indicate that the proposed algorithm really can predict prognoses of prostate cancer more accurately. The proposed two-stage FNN is able to learn the relationship between the clinical features and the prognosis of prostate cancer. Once the clinical data are known, the prognosis of prostate cancer patient can be predicted. Furthermore, unlike artificial neural networks, it is much easier to interpret the training results of the proposed network since they are in the form of fuzzy IF-THEN rules. These rules are very important for medical doctors. This can dramatically assist medical doctors to make decisions. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Smoothing neural network for constrained non-Lipschitz optimization with applications.

    Science.gov (United States)

    Bian, Wei; Chen, Xiaojun

    2012-03-01

    In this paper, a smoothing neural network (SNN) is proposed for a class of constrained non-Lipschitz optimization problems, where the objective function is the sum of a nonsmooth, nonconvex function, and a non-Lipschitz function, and the feasible set is a closed convex subset of . Using the smoothing approximate techniques, the proposed neural network is modeled by a differential equation, which can be implemented easily. Under the level bounded condition on the objective function in the feasible set, we prove the global existence and uniform boundedness of the solutions of the SNN with any initial point in the feasible set. The uniqueness of the solution of the SNN is provided under the Lipschitz property of smoothing functions. We show that any accumulation point of the solutions of the SNN is a stationary point of the optimization problem. Numerical results including image restoration, blind source separation, variable selection, and minimizing condition number are presented to illustrate the theoretical results and show the efficiency of the SNN. Comparisons with some existing algorithms show the advantages of the SNN.

  13. Exergy diagnosis of coal fired CHP plant with application of neural and regression modelling

    Directory of Open Access Journals (Sweden)

    Stanek Wojciech

    2012-01-01

    Full Text Available Mathematical models of the processes, that proceed in energetic machines and devices, in many cases are very complicated. In such cases, the exact analytical models should be equipped with the auxiliary empirical models that describe those parameters which are difficult to model in a theoretical way. Regression or neural models identified basing on measurements are rather simple and are characterized by relatively short computation time. For this reason they can be effectively applied for simulation and optimization of steering and regulation processes, as well as, for control and thermal diagnosis of operation (eq. power plants or CHP plants. In the paper regression and neural models of thermal processes developed for systems of operation control of thermal plants are presented. Theoretical-empirical model of processes proceeding in coal fired CHP plant have been applied. Simulative calculations basing on these models have been carried out. Results of simulative calculations have been used for the exergetic evaluation of considered power plant. The diagnosis procedure let to investigate the formation of exergy costs in interconnected components of the system of CHP, as well as, investigate the influence of defects in operation of components on exergy losses and on the exergetic cost in other components. [Acknowledgment. The paper has been prepared within the RECENT project (REsearch Center for Energy and New Technologies supported by 7th Framework Programme, Theme 4, Capacities.

  14. Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions

    Directory of Open Access Journals (Sweden)

    Krzysztof Okoń

    2001-01-01

    Full Text Available The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. Material for the study was obtained from patients operated on for pancreatic cancer, chronic pancreatitis and other tumours requiring pancreatic resection. Intraductal lesions were classified as low and high grade as previously described. The image analysis system consisted of a microscope, CCD camera combined with a PC and AnalySIS v. 2.11 software. The following texture characteristics were measured: variance of grey levels, features extracted from the grey levels correlation matrix and mean values, variance and standard deviation of the energy obtained from Laws matrices. Furthermore we used moments derived invariants and basic geometric data such as surface area, the minimum and maximum diameter and shape factor. The sets of data were randomly divided into training and testing groups. The training of the network using the back‐propagation algorithm, and the final classification of data was carried out with a neural network simulator SNNS v. 4.1. We studied the efficacy of networks containing from one to three hidden layers. Using the best network, containing three hidden layers, the rate of correct classification of nuclei was 73%, and the rate of misdiagnosis was 3%; in 24% the network response was ambiguous. The present findings may serve as a starting point in search for methods facilitating early diagnosis of ductal pancreatic carcinoma.

  15. A Recurrent Probabilistic Neural Network with Dimensional Reduction and Its Application to Time Series EEG Discrmination

    Science.gov (United States)

    Shima, Keisuke; Takata, Daisuke; Bu, Nan; Tsuji, Toshio

    This paper proposes a novel reduced-dimensional recurrent probabilistic neural network, and tries to classify electroencephalography (EEG) during motor images. In general, a recurrent probabilistic neural network (RPNN) is a useful tool for pattern discrimination of biological signals such as electromyograms (EMGs) and EEG due to its learning ability. However, when dealing with high dimensional data, RPNNs usually have problems of heavy computation burden and difficulty in training. To overcome these problems, the proposed RPNNs incorporates a dimension-reducing stage based on linear discriminant analysis into the network structure, and a hidden Markov model (HMM) and a Gaussian mixture model (GMM) are composed in the network structure for time-series discrimination. The proposed network is also applied to EEG discrimination using Laplacian filtering and wavelet packet transform (WPT). Discrimination experiments of EEG signals measured during calling motor images in mind were conducted with four subjects. The results showed that the proposed method can achieve relatively high discrimination performance (average discrimination rates: 84.6±5.9%), and indicated that the method has possibility to be applied for the human-machine interfaces.

  16. A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Si-Yao Fu

    2012-01-01

    Full Text Available In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs. By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people’s facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

  17. Neural analysis of seismic data: applications to the monitoring of Mt. Vesuvius

    Directory of Open Access Journals (Sweden)

    Antonietta M. Esposito

    2013-11-01

    Full Text Available The computing techniques currently available for the seismic monitoring allow advanced analysis. However, the correct event classification remains a critical aspect for the reliability of real time automatic analysis. Among the existing methods, neural networks may be considered efficient tools for detection and discrimination, and may be integrated into intelligent systems for the automatic classification of seismic events. In this work we apply an unsupervised technique for analysis and classification of seismic signals recorded in the Mt. Vesuvius area in order to improve the automatic event detection. The examined dataset contains about 1500 records divided into four typologies of events: earthquakes, landslides, artificial explosions, and “other” (any other signals not included in the previous classes. First, the Linear Predictive Coding (LPC and a waveform parametrization have been applied to achieve a significant and compact data encoding. Then, the clustering is obtained using a Self-Organizing Map (SOM neural network which does not require an a-priori classification of the seismic signals, groups those with similar structures, providing a simple framework for understanding the relationships between them. The resulting SOM map is separated into different areas, each one containing the events of a defined type. This means that the SOM discriminates well the four classes of seismic signals. Moreover, the system will classify a new input pattern depending on its position on the SOM map. The proposed approach can be an efficient instrument for the real time automatic analysis of seismic data, especially in the case of possible volcanic unrest.

  18. A surface EMG test tool to measure proportional prosthetic control.

    Science.gov (United States)

    Sturma, Agnes; Roche, Aidan D; Göbel, Peter; Herceg, Malvina; Ge, Nan; Fialka-Moser, Veronika; Aszmann, Oskar

    2015-06-01

    In upper limb amputees, prosthetic control training is recommended before and after fitting. During rehabilitation, the focus is on selective proportional control signals. For functional monitoring, many different tests are available. None can be used in the early phase of training. However, an early assessment is needed to judge if a patient has the potential to control a certain prosthetic set-up. This early analysis will determine if further training is needed or if other strategies would be more appropriate. Presented here is a tool that is capable of predicting achievable function in voluntary EMG control. This tool is applicable to individual muscle groups to support preparation of training and fitting. In four of five patients, the sEMG test tool accurately predicted the suitability for further myoelectric training based on SHAP outcome measures. (P1: "Poor" function in the sEMG test tool corresponded to 54/100 in the SHAP test; P2: Good: 85; P3: Good: 81; P4: Average: 78). One patient scored well during sEMG testing, but was unmotivated during SHAP testing. (Good: 50) Therefore, the surface EMG test tool may predict achievable control skills to a high extent, validated with the SHAP, but requires further clinical testing to validate this technique.

  19. Uptake of radiolabeled leukocytes in prosthetic graft infection

    Energy Technology Data Exchange (ETDEWEB)

    Serota, A.I.; Williams, R.A.; Rose, J.G.; Wilson, S.E.

    1981-07-01

    The utility of radionuclide labeled leukocytes in the demonstration of infection within vascular prostheses was examined. The infrarenal aorta was replaced with a 3 cm Dacron graft in 12 dogs. On the third postoperative day, six of the animals received an intravenous injection of 10(8) Staphylococcus aureus. Labeled leukocyte scans were performed at postoperative days one and three, and then weekly for 8 weeks with indium-111 and technetium-99 labeled autologous leukocytes. When scans showed focal uptake of isotope in the area of prosthetic material, the grafts were aseptically excised and cultured on mannitol-salt agar. Both control and infected animals had retroperitoneal isotope activity in the immediate postoperative period that disappeared by the end of the first week. By the eighth postoperative week, all of the animals that received the bacteremic challenge had both radionuclide concentration in the region of the vascular prosthesis and S. aureus cultured subsequently from the perigraft tissues. None of the control animals had either radionuclide or bacteriologic evidence of infection at the eighth postoperative week. The radiolabeled leukocyte scan is a highly sensitive and specific technique, clinically applicable for the diagnosis of vascular prosthetic infections.

  20. Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

    National Research Council Canada - National Science Library

    Dan Yang; Hailin Mu; Zengbing Xu; Zhigang Wang; Cancan Yi; Changming Liu

    2017-01-01

    ...) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL...

  1. An Analysis of the WAR AGAINST THE DISEASE-metaphor in Selected 2011 Presidential Speeches of Felipe Calderon with the Application of the Neural Theory of Metaphor

    Directory of Open Access Journals (Sweden)

    Agnieszka Pluwak

    2015-06-01

    Full Text Available An Analysis of the WAR AGAINST THE DISEASE-metaphor in Selected 2011 Presidential Speeches of Felipe Calderon with the Application of the Neural Theory of Metaphor The following paper describes the background of the neural theory of language by Jerome Feldman and provides a set of related research tools by George Lakoff. It also offers an example of its application to metaphor analysis based on 2011–2012 speeches by the Mexican President, Felipe Calderon. A complex WAR AGAINST THE DISEASE-metaphor is decomposed and explained with respect to cognitive patterns involved. In its envoy, the article goes back to a famous linguistic question, whether one can judge from linguistic research onto the mental and neural processes involved in language creation and processing. It is also an attempt to indicate possible future directions in the interdisciplinary field of metaphor research.

  2. Testing the applicability of neural networks as a gap-filling method using CH4 flux data from high latitude wetlands

    DEFF Research Database (Denmark)

    Dengel, S.; Zona, D.; Sachs, T.

    2013-01-01

    functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. Three different approaches were tested by including drivers such as air and soil temperature, barometric air...... included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach...... no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes, explain the method and test its...

  3. Cryogenic dark matter search (CDMS II): Application of neural networks and wavelets to event analysis

    Energy Technology Data Exchange (ETDEWEB)

    Attisha, Michael J. [Brown U.

    2006-01-01

    The Cryogenic Dark Matter Search (CDMS) experiment is designed to search for dark matter in the form of Weakly Interacting Massive Particles (WIMPs) via their elastic scattering interactions with nuclei. This dissertation presents the CDMS detector technology and the commissioning of two towers of detectors at the deep underground site in Soudan, Minnesota. CDMS detectors comprise crystals of Ge and Si at temperatures of 20 mK which provide ~keV energy resolution and the ability to perform particle identification on an event by event basis. Event identification is performed via a two-fold interaction signature; an ionization response and an athermal phonon response. Phonons and charged particles result in electron recoils in the crystal, while neutrons and WIMPs result in nuclear recoils. Since the ionization response is quenched by a factor ~ 3(2) in Ge(Si) for nuclear recoils compared to electron recoils, the relative amplitude of the two detector responses allows discrimination between recoil types. The primary source of background events in CDMS arises from electron recoils in the outer 50 µm of the detector surface which have a reduced ionization response. We develop a quantitative model of this ‘dead layer’ effect and successfully apply the model to Monte Carlo simulation of CDMS calibration data. Analysis of data from the two tower run March-August 2004 is performed, resulting in the world’s most sensitive limits on the spin-independent WIMP-nucleon cross-section, with a 90% C.L. upper limit of 1.6 × 10-43 cm2 on Ge for a 60 GeV WIMP. An approach to performing surface event discrimination using neural networks and wavelets is developed. A Bayesian methodology to classifying surface events using neural networks is found to provide an optimized method based on minimization of the expected dark matter limit. The discrete wavelet analysis of CDMS phonon pulses improves surface event discrimination in conjunction with the neural

  4. A repair algorithm for radial basis function neural network and its application to chemical oxygen demand modeling.

    Science.gov (United States)

    Qiao, Jun-Fei; Han, Hong-Gui

    2010-02-01

    This paper presents a repair algorithm for the design of a Radial Basis Function (RBF) neural network. The proposed repair RBF (RRBF) algorithm starts from a single prototype randomly initialized in the feature space. The algorithm has two main phases: an architecture learning phase and a parameter adjustment phase. The architecture learning phase uses a repair strategy based on a sensitivity analysis (SA) of the network's output to judge when and where hidden nodes should be added to the network. New nodes are added to repair the architecture when the prototype does not meet the requirements. The parameter adjustment phase uses an adjustment strategy where the capabilities of the network are improved by modifying all the weights. The algorithm is applied to two application areas: approximating a non-linear function, and modeling the key parameter, chemical oxygen demand (COD) used in the waste water treatment process. The results of simulation show that the algorithm provides an efficient solution to both problems.

  5. Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

    Science.gov (United States)

    Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan

    2017-06-26

    Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

  6. A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

    Science.gov (United States)

    Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan

    2014-01-01

    In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme. PMID:25202723

  7. Predictive control based on neural networks: an application to a fluid catalytic cracking industrial unit

    Directory of Open Access Journals (Sweden)

    V.M.L. Santos

    2000-12-01

    Full Text Available Artificial Neural Networks (ANNs constitute a technology that has recently become the focus of great attention. The reason for this is due mainly to its capacity to treat complex and nonlinear problems. This work consists of the identification and control of a fluid cracking catalytic unit (FCCU using techniques based on multilayered ANNs. The FCC unit is a typical example of a complex and nonlinear process, possessing great interaction among the operation variables and many operational constraints to be attended. Model Predictive Control is indicated in these occasions. The FCC model adopted was validated with plant data by Moro (1992; and was used in this work to replace the real process in the generation of data for the identification of the ANNs and to test the predictive control strategy. The results of the identification and control of the process through ANNs indicate the viability of the technique.

  8. Application of neural networks and support vector machine for significant wave height prediction

    Directory of Open Access Journals (Sweden)

    Jadran Berbić

    2017-07-01

    Full Text Available For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN and support vector machine (SVM. The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

  9. A RBF neural network model with GARCH errors: Application to electricity price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Coelho, Leandro dos Santos [Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Parana, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos, Andre A.P. [Department of Statistics, Universidad Carlos III de Madrid, C/ Madrid, 126, 28903 Getafe, Madrid (Spain)

    2011-01-15

    In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices. (author)

  10. Application of artificial neural networks to identify equilibration in computer simulations

    Science.gov (United States)

    Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

    2017-11-01

    Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

  11. A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

    Directory of Open Access Journals (Sweden)

    Zhekang Dong

    2014-01-01

    Full Text Available In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

  12. Firing Rate Estimation Using Infinite Mixture Models and Its Application to Neural Decoding.

    Science.gov (United States)

    Shibue, Ryohei; Komaki, Fumiyasu

    2017-08-09

    Neural decoding is a framework for reconstructing external stimuli from spike trains recorded in brains. Kloosterman et al. (2014) proposed a new decoding method using marked point processes. This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate intensity functions of marked point processes. However, using kernel density estimation causes problems. To overcome these problems, we propose a new decoding method using infinite mixture models to estimate intensity. The proposed method improves decoding performance in terms of accuracy and computation speed. We apply the proposed method to simulation and experimental data to verify its performance. Copyright © 2016, Journal of Neurophysiology.

  13. Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors

    Energy Technology Data Exchange (ETDEWEB)

    Flores, J.L. [Dpto de Ingeniería Eléctrica y Térmica, Universidad de Huelva (Spain); Martel, I. [Dpto de Física Aplicada, Universidad de Huelva (Spain); CERN, ISOLDE, CH 1211 Geneva, 23 (Switzerland); Jiménez, R. [Dpto de Ingeniería Electrónica, Sist. Informáticos y Automática, Universidad de Huelva (Spain); Galán, J., E-mail: jgalan@diesia.uhu.es [Dpto de Ingeniería Electrónica, Sist. Informáticos y Automática, Universidad de Huelva (Spain); Salmerón, P. [Dpto de Ingeniería Eléctrica y Térmica, Universidad de Huelva (Spain)

    2016-09-11

    The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from {sup 12}C up to {sup 84}Kr, yielding higher discrimination rates than any other previously reported.

  14. [Application of an artificial neural network in the design of sustained-release dosage forms].

    Science.gov (United States)

    Wei, X H; Wu, J J; Liang, W Q

    2001-09-01

    To use the artificial neural network (ANN) in Matlab 5.1 tool-boxes to predict the formulations of sustained-release tablets. The solubilities of nine drugs and various ratios of HPMC: Dextrin for 63 tablet formulations were used as the ANN model input, and in vitro accumulation released at 6 sampling times were used as output. The ANN model was constructed by selecting the optimal number of iterations (25) and model structure in which there are one hidden layer and five hidden layer nodes. The optimized ANN model was used for prediction of formulation based on desired target in vitro dissolution-time profiles. ANN predicted profiles based on ANN predicted formulations were closely similar to the target profiles. The ANN could be used for predicting the dissolution profiles of sustained release dosage form and for the design of optimal formulation.

  15. Short-term load forecasting using neural network for future smart grid application

    Science.gov (United States)

    Zennamo, Joseph Anthony, III

    Short-term load forecasting of power system has been a classic problem for a long time. Not merely it has been researched extensively and intensively, but also a variety of forecasting methods has been raised. This thesis outlines some aspects and functions of smart meter. It also presents different policies and current statuses as well as future projects and objectives of SG development in several countries. Then the thesis compares main aspects about latest products of smart meter from different companies. Lastly, three types of prediction models are established in MATLAB to emulate the functions of smart grid in the short-term load forecasting, and then their results are compared and analyzed in terms of accuracy. For this thesis, more variables such as dew point temperature are used in the Neural Network model to achieve more accuracy for better short-term load forecasting results.

  16. Calibration of neural networks using genetic algorithms, with application to optimal path planning

    Science.gov (United States)

    Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel

    1987-01-01

    Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.

  17. Identification of putative domain linkers by a neural network – application to a large sequence database

    Directory of Open Access Journals (Sweden)

    Kuroda Yutaka

    2006-06-01

    Full Text Available Background The reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences. Results Our search detected 3009 putative domain linkers adjacent to or overlapping with domains, as defined by sequence similarity to either Protein Data Bank (PDB or Conserved Domain Database (CDD sequences. Among these putative linkers, 75% were "correctly" located within 20 residues of a domain terminus, and the remaining 25% were found in the middle of a domain, and probably represented failed predictions. Moreover, our neural network predicted 5124 putative domain linkers in structurally un-annotated regions without sequence similarity to PDB or CDD sequences, which suggest to the possible existence of novel structural domains. As a comparison, we performed the same analysis by identifying low-complexity regions (LCR, which are known to encode unstructured polypeptide segments, and observed that the fraction of LCRs that correlate with domain termini is similar to that of domain linkers. However, domain linkers and LCRs appeared to identify different types of domain boundary regions, as only 32% of the putative domain linkers overlapped with LCRs. Conclusion Overall, our study indicates that the two methods detect independent and complementary regions, and that the combination of these methods can substantially improve the sensitivity of the domain boundary prediction. This finding should enable the identification of novel structural domains, yielding new targets for large scale protein analyses.

  18. The application of artificial neural network model in the non-invasive diagnosis of liver fibrosis

    Directory of Open Access Journals (Sweden)

    Bo LI

    2012-12-01

    Full Text Available Objective  To construct and evaluate an artificial neural network (ANN model as a new non-invasive diagnostic method for clinical assessment of liver fibrosis at early stage. Methods  The model was set up and tested among 683 chronic hepatitis B (CHB patients, with authentic positive clinical biopsy results, proved to have liver fibrosis or cirrhosis, admitted to 302 Hospital of PLA from May 2008 to March 2011. Among 683 samples, 504 samples were diagnosed as cirrhosis as a result of CHB, and 179 liver fibrosis due to other liver diseases. 134 out of 683 patients were included in training group by stratified sampling, and the others for verification. Six items (age, AST, PTS, PLT, GGT and DBil were selected as input layer indexes to set up the model for evaluation. Results  The ANN model for diagnosis of liver fibrosis was set up. The diagnostic accuracy was 77.4%, sensitivity was 76.8%, and specificity was 77.8%. Its Kappa concordance tests showed the diagnosis result of the model was consistent with biopsy result (Kappa index=0.534. The accuracy, sensitivity and specificity of CHB patients were 80.4%, 79.9% and 80.7% (Kappa index=0.598 respectively, and those for other liver diseases were 67.9%, 64.3% and 69.7% (Kappa index=0.316. Conclusion  The artificial neural network model established by the authors demonstrates its high sensitivity and specificity as a new non-invasive diagnostic method for liver fibrosis induced by HBV infection. However, it shows limited diagnostic reliability to fibrosis as a result of other liver diseases.

  19. Biologically inspired multi-layered synthetic skin for tactile feedback in prosthetic limbs.

    Science.gov (United States)

    Osborn, Luke; Nguyen, Harrison; Betthauser, Joseph; Kaliki, Rahul; Thakor, Nitish

    2016-08-01

    The human body offers a template for many state-of-the-art prosthetic devices and sensors. In this work, we present a novel, sensorized synthetic skin that mimics the natural multi-layered nature of mechanoreceptors found in healthy glabrous skin to provide tactile information. The multi-layered sensor is made up of flexible piezoresistive textiles that act as force sensitive resistors (FSRs) to convey tactile information, which are embedded within a silicone rubber to resemble the compliant nature of human skin. The top layer of the synthetic skin is capable of detecting small loads less than 5 N whereas the bottom sensing layer responds reliably to loads over 7 N. Finite element analysis (FEA) of a simplified human fingertip and the synthetic skin was performed. Results suggest similarities in behavior during loading. A natural tactile event is simulated by loading the synthetic skin on a prosthetic limb. Results show the sensors' ability to detect applied loads as well as the ability to simulate neural spiking activity based on the derivative and temporal differences of the sensor response. During the tactile loading, the top sensing layer responded 0.24 s faster than the bottom sensing layer. A synthetic biologically-inspired skin such as this will be useful for enhancing the functionality of prosthetic limbs through tactile feedback.

  20. Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1).

    Science.gov (United States)

    Caballero, Julio; Fernández, Michael

    2008-01-01

    Artificial neural networks (ANNs) have been widely used for medicinal chemistry modeling. In the last two decades, too many reports used MATLAB environment as an adequate platform for programming ANNs. Some of these reports comprise a variety of applications intended to quantitatively or qualitatively describe structure-activity relationships. A powerful tool is obtained when there are combined Bayesian-regularized neural networks (BRANNs) and genetic algorithm (GA): Bayesian-regularized genetic neural networks (BRGNNs). BRGNNs can model complicated relationships between explanatory variables and dependent variables. Thus, this methodology is regarded as useful tool for QSAR analysis. In order to demonstrate the use of BRGNNs, we developed a reliable method for predicting the antagonistic activity of 5-amino-3-arylisoxazole derivatives against Human Platelet Thrombin Receptor (PAR-1), using classical 3D-QSAR methodologies: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). In addition, 3D vectors generated from the molecular structures were correlated with antagonistic activities by multivariate linear regression (MLR) and Bayesian-regularized neural networks (BRGNNs). All models were trained with 34 compounds, after which they were evaluated for predictive ability with additional 6 compounds. CoMFA and CoMSIA were unable to describe this structure-activity relationship, while BRGNN methodology brings the best results according to validation statistics.

  1. Retinal Prosthetics, Optogenetics, and Chemical Photoswitches

    Science.gov (United States)

    2015-01-01

    Three technologies have emerged as therapies to restore light sensing to profoundly blind patients suffering from late-stage retinal degenerations: (1) retinal prosthetics, (2) optogenetics, and (3) chemical photoswitches. Prosthetics are the most mature and the only approach in clinical practice. Prosthetic implants require complex surgical intervention and provide only limited visual resolution but can potentially restore navigational ability to many blind patients. Optogenetics uses viral delivery of type 1 opsin genes from prokaryotes or eukaryote algae to restore light responses in survivor neurons. Targeting and expression remain major problems, but are potentially soluble. Importantly, optogenetics could provide the ultimate in high-resolution vision due to the long persistence of gene expression achieved in animal models. Nevertheless, optogenetics remains challenging to implement in human eyes with large volumes, complex disease progression, and physical barriers to viral penetration. Now, a new generation of photochromic ligands or chemical photoswitches (azobenzene-quaternary ammonium derivatives) can be injected into a degenerated mouse eye and, in minutes to hours, activate light responses in neurons. These photoswitches offer the potential for rapidly and reversibly screening the vision restoration expected in an individual patient. Chemical photoswitch variants that persist in the cell membrane could make them a simple therapy of choice, with resolution and sensitivity equivalent to optogenetics approaches. A major complexity in treating retinal degenerations is retinal remodeling: pathologic network rewiring, molecular reprogramming, and cell death that compromise signaling in the surviving retina. Remodeling forces a choice between upstream and downstream targeting, each engaging different benefits and defects. Prosthetics and optogenetics can be implemented in either mode, but the use of chemical photoswitches is currently limited to downstream

  2. Retinal prosthetics, optogenetics, and chemical photoswitches.

    Science.gov (United States)

    Marc, Robert; Pfeiffer, Rebecca; Jones, Bryan

    2014-10-15

    Three technologies have emerged as therapies to restore light sensing to profoundly blind patients suffering from late-stage retinal degenerations: (1) retinal prosthetics, (2) optogenetics, and (3) chemical photoswitches. Prosthetics are the most mature and the only approach in clinical practice. Prosthetic implants require complex surgical intervention and provide only limited visual resolution but can potentially restore navigational ability to many blind patients. Optogenetics uses viral delivery of type 1 opsin genes from prokaryotes or eukaryote algae to restore light responses in survivor neurons. Targeting and expression remain major problems, but are potentially soluble. Importantly, optogenetics could provide the ultimate in high-resolution vision due to the long persistence of gene expression achieved in animal models. Nevertheless, optogenetics remains challenging to implement in human eyes with large volumes, complex disease progression, and physical barriers to viral penetration. Now, a new generation of photochromic ligands or chemical photoswitches (azobenzene-quaternary ammonium derivatives) can be injected into a degenerated mouse eye and, in minutes to hours, activate light responses in neurons. These photoswitches offer the potential for rapidly and reversibly screening the vision restoration expected in an individual patient. Chemical photoswitch variants that persist in the cell membrane could make them a simple therapy of choice, with resolution and sensitivity equivalent to optogenetics approaches. A major complexity in treating retinal degenerations is retinal remodeling: pathologic network rewiring, molecular reprogramming, and cell death that compromise signaling in the surviving retina. Remodeling forces a choice between upstream and downstream targeting, each engaging different benefits and defects. Prosthetics and optogenetics can be implemented in either mode, but the use of chemical photoswitches is currently limited to downstream

  3. Prosthetic Hand With Two Gripping Fingers

    Science.gov (United States)

    Norton, William E.; Belcher, Jewell B.; Vest, Thomas W.; Carden, James R.

    1993-01-01

    Prosthetic hand developed for amputee who retains significant portion of forearm. Outer end of device is end effector including two fingers, one moved by rotating remaining part of forearm about its longitudinal axis. Main body of end effector is end member supporting fingers, roller bearing assembly, and rack-and-pinion mechanism. Advantage of rack-and-pinion mechanism enables user to open or close gap between fingers with precision and force.

  4. Prosthetic management of an ocular defect

    Directory of Open Access Journals (Sweden)

    Siddesh Kumar Chintal

    2010-01-01

    Full Text Available The disfigurement associated with the loss of an eye can cause significant physical and emotional problems. Various treatment modalities are available, one of which is implants. Although implant has a superior outcome, it may not be advisable in all patients due to economic factors. The present article describes the prosthetic management of an ocular defect with a custom-made ocular prosthesis.

  5. The Prosthetic Workflow in the Digital Era

    OpenAIRE

    Lidia Tordiglione; Michele De Franco; Giovanni Bosetti

    2016-01-01

    The purpose of this retrospective study was to clinically evaluate the benefits of adopting a full digital workflow for the implementation of fixed prosthetic restorations on natural teeth. To evaluate the effectiveness of these protocols, treatment plans were drawn up for 15 patients requiring rehabilitation of one or more natural teeth. All the dental impressions were taken using a Planmeca PlanScan® (Planmeca OY, Helsinki, Finland) intraoral scanner, which provided digital casts on which t...

  6. New developments in prosthetic arm systems

    Directory of Open Access Journals (Sweden)

    Vujaklija I

    2016-07-01

    Full Text Available Ivan Vujaklija,1 Dario Farina,1 Oskar C Aszmann2 1Institute of Neurorehabilitation Systems, Bernstein Focus Neurotechnology Göttingen, University Medical Center Göttingen, Georg-August University, Göttingen, Germany; 2Christian Doppler Laboratory for Restoration of Extremity Function, Division of Plastic and Reconstructive Surgery, Department of Surgery, Medical University of Vienna, Vienna, Austria Abstract: Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold – insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb

  7. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Cha, Kenny H.; Richter, Caleb D.

    2017-12-01

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aim of translating the ‘knowledge’ learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With Institutional Review Board (IRB) approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2242 views with 2454 masses (1057 malignant, 1397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p  =  0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited.

  8. Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

    Science.gov (United States)

    Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Cha, Kenny; Richter, Caleb

    2017-10-16

    Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-task transfer learning DCNN with the aims of translating the 'knowledge' learned from non-medical images to medical diagnostic tasks through supervised training and increasing the generalization capabilities of DCNNs by simultaneously learning auxiliary tasks. We studied this approach in an important application: classification of malignant and benign breast masses. With IRB approval, digitized screen-film mammograms (SFMs) and digital mammograms (DMs) were collected from our patient files and additional SFMs were obtained from the Digital Database for Screening Mammography. The data set consisted of 2,242 views with 2,454 masses (1,057 malignant, 1,397 benign). In single-task transfer learning, the DCNN was trained and tested on SFMs. In multi-task transfer learning, SFMs and DMs were used to train the DCNN, which was then tested on SFMs. N-fold cross-validation with the training set was used for training and parameter optimization. On the independent test set, the multi-task transfer learning DCNN was found to have significantly (p=0.007) higher performance compared to the single-task transfer learning DCNN. This study demonstrates that multi-task transfer learning may be an effective approach for training DCNN in medical imaging applications when training samples from a single modality are limited. © 2017 Institute of Physics and Engineering in Medicine.

  9. Adaptive sports technology and biomechanics: prosthetics.

    Science.gov (United States)

    De Luigi, Arthur Jason; Cooper, Rory A

    2014-08-01

    With the technologic advances in medicine and an emphasis on maintaining physical fitness, the population of athletes with impairments is growing. It is incumbent upon health care practitioners to make every effort to inform these individuals of growing and diverse opportunities and to encourage safe exercise and athletic participation through counseling and education. Given the opportunities for participation in sports for persons with a limb deficiency, the demand for new, innovative prosthetic designs is challenging the clinical and technical expertise of the physician and prosthetist. When generating a prosthetic prescription, physicians and prosthetists should consider the needs and preferences of the athlete with limb deficiency, as well as the functional demands of the chosen sporting activity. The intent of this article is to provide information regarding the current advancements in the adaptive sports technology and biomechanics in the field of prosthetics, and to assist clinicians and their patients in facilitating participation in sporting activities. Copyright © 2014 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

  10. The Prosthetic Experience Between Body and Technology

    DEFF Research Database (Denmark)

    Søndergaard, Morten

    2018-01-01

    In this paper, I argue that a prosthetic aesthetic instigated by experimental art practices operate with and within a ‘second nature’ – in-between science and art. Drawing on theories from Dewey and Edelman and examples from Da Vinci, Brancusi, Man Ray, Dali and Stelarc, I am calling for an exper...... in art, which is performing a complex re-design of (the idea and representation of) technology and the body.......In this paper, I argue that a prosthetic aesthetic instigated by experimental art practices operate with and within a ‘second nature’ – in-between science and art. Drawing on theories from Dewey and Edelman and examples from Da Vinci, Brancusi, Man Ray, Dali and Stelarc, I am calling...... for an experience-based analysis of experimental practices operating between body and technology. These practices, which, rather than falling into the category of science fiction or horror cinema as some recent critique from post-human studies would have it, are pointing towards a genealogy of prosthetic experience...

  11. The Application of Cognitive Diagnostic Approaches via Neural Network Analysis of Serious Educational Games

    Science.gov (United States)

    Lamb, Richard L.

    Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student

  12. Wide field imaging - I. Applications of neural networks to object detection and star/galaxy classification

    Science.gov (United States)

    Andreon, S.; Gargiulo, G.; Longo, G.; Tagliaferri, R.; Capuano, N.

    2000-12-01

    Astronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NExt (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of `what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NExt procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features

  13. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

    Science.gov (United States)

    Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M

    2014-05-07

    Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results

  14. Development of a BIONic muscle spindle for prosthetic proprioception.

    Science.gov (United States)

    Sachs, Nicholas A; Loeb, Gerald E

    2007-06-01

    The replacement of proprioceptive function, whether for conscious sensation or feedback control, is likely to be an important aspect of neural prosthetic restoration of limb movements. Thus far, however, it has been hampered by the absence of unobtrusive sensors. We propose a method whereby fully implanted, telemetrically operated BIONs monitor muscle movement, and thereby detect changes in joint angle(s) and/or limb posture without requiring the use of secondary components attached to limb segments or external reference frames. The sensor system is designed to detect variations in the electrical coupling between devices implanted in neighboring muscles that result from changes in their relative position as the muscles contract and stretch with joint motion. The goal of this study was to develop and empirically validate mathematical models of the sensing scheme and to use computer simulations to provide an early proof of concept and inform design of the overall sensor system. Results from experiments using paired dipoles in a saline bath and finite element simulations have given insight into the current distribution and potential gradients exhibited within bounded anisotropic environments similar to a human limb segment and demonstrated an anticipated signal to noise ratio of at least 8:1 for submillimeter resolution of relative implant movement over a range of implant displacements up to 15 cm.

  15. Using Adaptive Neural-Fuzzy Inference Systems (ANFIS for Demand Forecasting and an Application

    Directory of Open Access Journals (Sweden)

    Onur Doğan

    2016-06-01

    Full Text Available Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

  16. Application of an artificial neural network model for selection of potential lung cancer biomarkers.

    Science.gov (United States)

    Ligor, Tomasz; Pater, Łukasz; Buszewski, Bogusław

    2015-05-06

    Determination of volatile organic compounds (VOCs) in the exhaled breath samples of lung cancer patients and healthy controls was carried out by SPME-GC/MS (solid phase microextraction- gas chromatography combined with mass spectrometry) analyses. In order to compensate for the volatile exogenous contaminants, ambient air blank samples were also collected and analyzed. We recruited a total of 123 patients with biopsy-confirmed lung cancer and 361 healthy controls to find the potential lung cancer biomarkers. Automatic peak deconvolution and identification were performed using chromatographic data processing software (AMDIS with NIST database). All of the VOCs sample data operation, storage and management were performed using the SQL (structured query language) relational database. The selected eight VOCs could be possible biomarker candidates. In cross-validation on test data sensitivity was 63.5% and specificity 72.4% AUC 0.65. The low performance of the model has been mainly due to overfitting and the exogenous VOCs that exist in breath. The dedicated software implementing a multilayer neural network using a genetic algorithm for training was built. Further work is needed to confirm the performance of the created experimental model.

  17. Neural networks-based operational prototype for flash flood forecasting: application to Liane flash floods (France

    Directory of Open Access Journals (Sweden)

    Bertin Dominique

    2016-01-01

    Full Text Available The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogne-sur-Mer. Due to the complexity of land cover and hydrologic processes, a black-box non-linear modelling was chosen using neural networks. The multilayer perceptron model, known for its property of universal approximation is thus chosen. Four models were designed, each one for one forecasting horizon using rainfall forecasts: 24h, 12h, 6h, 3h. The desired output of the model is original: it represents the maximal value of the water level respectively 24h, 12h, 6h, 3h ahead. Working with best forecasts of rain (the observed ones during the event in the past, on the major flood of the database in test set, the model provides excellent forecasts. Nash criteria calculated for the four lead times are 0.98 (3h, 0.97 (6h, 0.91 (12h, 0.89 (24h. Designed models were thus estimated as efficient enough to be implemented in a specific tool devoted to real time operational use. The software tool is described hereafter: designed in Java, it presents a friendly interface allowing applying various scenarios of future rainfalls, and a graphical visualization of the predicted maximum water levels and their associated real time observed values.

  18. Convolutional neural networks for P300 detection with application to brain-computer interfaces.

    Science.gov (United States)

    Cecotti, Hubert; Gräser, Axel

    2011-03-01

    A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.

  19. Application of neural networks and information theory to the identification of E. coli transcriptional promoters

    Energy Technology Data Exchange (ETDEWEB)

    Abremski, K. (Du Pont Merck Pharmaceutical Co., Wilmington, DE (USA). Experimental Station); Sirotkin, K. (National Center for Biotechnology Information, Bethesda, MD (USA)); Lapedes, A. (Los Alamos National Lab., NM (USA))

    1991-01-01

    The Humane Genome Project has as its eventual goal the determination of the entire DNA sequence of man, which comprises approximately 3 billion base pairs. An important aspect of this project will be the analysis of the sequence to locate regions of biological importance. New computer methods will be needed to automate and facilitate this task. In this paper, we have investigated use of neural networks for the recognition of functional patterns in biological sequences. The prediction of Escherichia coli transcriptional promoters was chosen as a model system for these studies. Two approaches were employed. In the fist method, a mutual information analysis of promoter and nonpromoter sequences was carried out to demonstrate the informative base positions that help to distinguish promoter sequences from non-promoter sequences. These base positions were than used to train a Perceptron to predict new promoter sequences. In the second method, the experimental knowledge of promoters was used to indicate the important base positions in the sequence. These base positions were used to train a back propagation network with hidden units which represented regions of sequence conservation found in promoters. With both types of networks, prediction of new promoter sequences was greater than 96.9%. 12 refs., 1 fig., 4 tabs.

  20. Application of Artificial Neural Network to Predict Colour Change, Shrinkage and Texture of Osmotically Dehydrated Pumpkin

    Science.gov (United States)

    Tang, S. Y.; Lee, J. S.; Loh, S. P.; Tham, H. J.

    2017-06-01

    The objectives of this study were to use Artificial Neural Network (ANN) to predict colour change, shrinkage and texture of osmotically dehydrated pumpkin slices. The effects of process variables such as concentration of osmotic solution, immersion temperature and immersion time on the above mentioned physical properties were studied. The colour of the samples was measured using a colorimeter and the net colour difference changes, ΔE were determined. The texture was measured in terms of hardness by using a Texture Analyzer. As for the shrinkage, displacement of volume method was applied and percentage of shrinkage was obtained in terms of volume changes. A feed-forward backpropagation network with sigmoidal function was developed and best network configuration was chosen based on the highest correlation coefficients between the experimental values versus predicted values. As a comparison, Response Surface Methodology (RSM) statistical analysis was also employed. The performances of both RSM and ANN modelling were evaluated based on absolute average deviation (AAD), correlation of determination (R2) and root mean square error (RMSE). The results showed that ANN has higher prediction capability as compared to RSM. The relative importance of the variables on the physical properties were also determined by using connection weight approach in ANN. It was found that solution concentration showed the highest influence on all three physical properties.

  1. Application of neural network to liver magnetic-resonance-imaging study

    Science.gov (United States)

    Ong, Chin-Sing; Chu, Wei-Kom; Anderson, Joseph C.; Syh, Hon-Wei

    1992-09-01

    Magnetic resonance imaging (MRI) of the liver has demonstrated to be quite sensitive in showing Hepatic Hemangioma as high intensity lesions in T2 weighted imaging sequence. Hepatic Hemangioma is a non-malignant tumor and has relative high occurrence rate among the general population. It is of importance to differentiate this benign abnormality from other high intensity malignant lesions, such as hepatoma, adenocarcinoma, or metastasis. The objective of our study was to investigate the feasibility of applying neural network to assist in the differentiation of the liver MRI lesions. Thirty-seven liver MRI studies were collected, this including twenty-three cases of hepatic hemangioma and fourteen cases of malignant tumors. all cases were clinically proven with the diagnosed pathological condition and verified by biopsy. Four quantitative features, adopted from published literatures and used clinically on a routine basis, were measured from MRI images. In this study, a multilayer and two layer backpropagation networks were used for performance comparison. By attempting various training methods, the accuracy of the two layer network had been improved from 74% to 83% by selecting the proper boundary set based on the euclidean distance for each data set in both classes when training the network.

  2. Development and application of Artificial Neural Networks in forecasting the maximum daily precipitation at Athens, Greece

    Science.gov (United States)

    Nastos, P. T.; Paliatsos, A. G.; Larissi, I. K.; Moustris, K. P.

    2012-04-01

    Extreme daily precipitation events are involved in significant environmental damages, even in life loss, because of causing adverse impacts, such as flash floods, in urban and sometimes in rural areas. Thus, long-term forecast of such events is of great importance, in order to be prepared the local authorities to confront and mitigate the adverse consequences. The objective of this study is to estimate the possibility of forecasting the maximum daily precipitation for the next coming year. For this reason, appropriate prognostic models, such as Artificial Neural Networks (ANNs) were developed and applied. The data used for the analysis concern daily precipitation totals, which have been recorded at National Observatory of Athens (NOA), during the period 1891-2009. To evaluate the potential of daily extreme precipitation prognosis by the applied ANNs, a different period was considered than the one used for the ANNs training. Thus, the datasets of the period 1891-1980 were used as training datasets, while the datasets of the period 1981-2009 as validation datasets. Appropriate statistical indices, such as the Coefficient of Determination (R2), the Index of Agreement (IA), the Root Mean Square Error (RMSE), and the Mean Bias Error (MBE) were applied to test the reliability of the models. The findings of the analysis showed that, a quite satisfactory relationship at the statistically significant level of p<0.01 appears between the forecasted maximum daily precipitation totals for the next coming year and the respective observed ones.

  3. Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine

    Directory of Open Access Journals (Sweden)

    Marcin Strączkiewicz

    2016-01-01

    Full Text Available In the monitoring process of wind turbines the utmost attention should be given to gearboxes. This conclusion is derived from numerous summary papers. They reveal that, on the one hand, gearboxes are one of the most fault susceptible elements in the drive-train and, on the other, the most expensive to replace. Although state-of-the-art CMS can usually provide advanced signal processing tools for extraction of diagnostic information, there are still many installations, where the diagnosis is based simply on the averaged wideband features like root-mean-square (RMS or peak-peak (PP. Furthermore, for machinery working in highly changing operational conditions, like wind turbines, those estimators are strongly fluctuating, and this fluctuation is not linearly correlated to operation parameters. Thus, the sudden increase of a particular feature does not necessarily have to indicate the development of fault. To overcome this obstacle, it is proposed to detect a fault development with Artificial Neural Network (ANN and further observation of linear regression parameters calculated on the estimation error between healthy and unknown condition. The proposed reasoning is presented on the real life example of ring gear fault in wind turbine’s planetary gearbox.

  4. Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

    Directory of Open Access Journals (Sweden)

    Vladimir Krasnopolsky

    2016-01-01

    Full Text Available A neural network (NN technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013, and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN’s generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

  5. The Application of Neural Networks in Balancing Production of Crude Sunflower Oil and Meal

    Directory of Open Access Journals (Sweden)

    Bojan Ivetic

    2014-08-01

    Full Text Available The aim of the research is to predict specific output characteristics of half finished goods (crude sunflower oil and meal on the basis of specific input variables (quality and composition of sunflower seeds, with the help of artificial neural networks. This is an attempt to predict the amount much more precisely than is the case with technological calculations commonly used in the oil industry. All input variables are representing the data received by the laboratory, and the output variables except category % of oil which is obtained by measuring the physical quantity of produced crude sunflower oil and sunflower consumed quantity of the processing quality. The correct prediction of the output variables contributes to better sales planning, production of sunflower oil, and better use of storage. Also, the correct prediction of technological results of the quality of crude oil and meal provides timely response and also preventing getting rancid and poor-quality oil, timely categorizing meal, which leads to proper planning and sales to the rational utilization of storage space, allows timely response technologists and prevents the growth of microorganisms in the meal.

  6. Deep biomarkers of human aging: Application of deep neural networks to biomarker development.

    Science.gov (United States)

    Putin, Evgeny; Mamoshina, Polina; Aliper, Alexander; Korzinkin, Mikhail; Moskalev, Alexey; Kolosov, Alexey; Ostrovskiy, Alexander; Cantor, Charles; Vijg, Jan; Zhavoronkov, Alex

    2016-05-01

    One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R(2) = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R(2) = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

  7. Modeling of Soft sensor based on Artificial Neural Network for Galactic Cosmic Rays Application

    Science.gov (United States)

    Suparta, W.; Putro, W. S.

    2014-10-01

    For successful designing of space radiation Galactic Cosmic Rays (GCRs) model, we develop a soft sensor based on the Artificial Neural Network (ANN) model. At the first step, the soft sensor based ANN was constructed as an alternative to model space radiation environment. The structure of ANN in this model is using Multilayer Perceptron (MLP) and Levenberg Marquardt algorithms with 3 inputs and 2 outputs. In the input variable, we use 12 years data (Corr, Uncorr and Press) of GCR particles obtained from Neutron Monitor of Bartol University (Fort Smith area) and the target output is (Corr and Press) from the same source but for Inuvik area in the Polar Regions. In the validation step, we obtained the Root Mean Square Error (RMSE) value of Corr 3.8670e-004 and Press 1.3414e-004 and Variance Accounted For (VAF) of Corr 99.9839 % and Press 99.9831% during the training section. After all the results obtained, then we applied into a Matlab GUI simulation (soft sensor simulation). This simulation will display the estimation of output value from input (Corr and Press). Testing results showed an error of 0.133% and 0.014% for Corr and Press, respectively.

  8. Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.

    Science.gov (United States)

    Umehara, Kensuke; Ota, Junko; Ishida, Takayuki

    2017-10-18

    In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.

  9. Application of different entropy formalisms in a neural network for novel word learning

    Science.gov (United States)

    Khordad, R.; Rastegar Sedehi, H. R.

    2015-12-01

    In this paper novel word learning in adults is studied. For this goal, four entropy formalisms are employed to include some degree of non-locality in a neural network. The entropy formalisms are Tsallis, Landsberg-Vedral, Kaniadakis, and Abe entropies. First, we have analytically obtained non-extensive cost functions for the all entropies. Then, we have used a generalization of the gradient descent dynamics as a learning rule in a simple perceptron. The Langevin equations are numerically solved and the error function (learning curve) is obtained versus time for different values of the parameters. The influence of index q and number of neuron N on learning is investigated for the all entropies. It is found that learning is a decreasing function of time for the all entropies. The rate of learning for the Landsberg-Vedral entropy is slower than other entropies. The variation of learning with time for the Landsberg-Vedral entropy is not appreciable when the number of neurons increases. It is said that entropy formalism can be used as a means for studying the learning.

  10. Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications.

    Science.gov (United States)

    Pani, Danilo; Barabino, Gianluca; Citi, Luca; Meloni, Paolo; Raspopovic, Stanisa; Micera, Silvestro; Raffo, Luigi

    2016-09-01

    The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point digital signal processor (DSP). The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 h processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

  11. Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

    Directory of Open Access Journals (Sweden)

    Dilek Göksel-Duru

    2013-01-01

    Full Text Available Diffusion tensor magnetic resonance imaging (DTMRI as a noninvasive modality providing in vivo anatomical information allows determination of fiber connections which leads to brain mapping. The success of DTMRI is very much algorithm dependent, and its verification is of great importance due to limited availability of a gold standard in the literature. In this study, unsupervised artificial neural network class, namely, self-organizing maps, is employed to discover the underlying fiber tracts. A common artificial diffusion tensor resource, named “phantom images for simulating tractography errors” (PISTE, is used for the accuracy verification and acceptability of the proposed approach. Four different tract geometries with varying SNRs and fractional anisotropy are investigated. The proposed method, SOFMAT, is able to define the predetermined fiber paths successfully with a standard deviation of (0.8–1.9 × 10−3 depending on the trajectory and the SNR value selected. The results illustrate the capability of SOFMAT to reconstruct complex fiber tract configurations. The ability of SOFMAT to detect fiber paths in low anisotropy regions, which physiologically may correspond to either grey matter or pathology (abnormality and uncertainty areas in real data, is an advantage of the method for future studies.

  12. A probablistic neural network classification system for signal and image processing

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, B. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  13. Classification of EMG signals using artificial neural networks for virtual hand prosthesis control.

    Science.gov (United States)

    Mattioli, Fernando E R; Lamounier, Edgard A; Cardoso, Alexandre; Soares, Alcimar B; Andrade, Adriano O

    2011-01-01

    Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

  14. Face recognition in simulated prosthetic vision: face detection-based image processing strategies

    Science.gov (United States)

    Wang, Jing; Wu, Xiaobei; Lu, Yanyu; Wu, Hao; Kan, Han; Chai, Xinyu

    2014-08-01

    Objective. Given the limited visual percepts elicited by current prosthetic devices, it is essential to optimize image content in order to assist implant wearers to achieve better performance of visual tasks. This study focuses on recognition of familiar faces using simulated prosthetic vision. Approach. Combined with region-of-interest (ROI) magnification, three face extraction strategies based on a face detection technique were used: the Viola-Jones face region, the statistical face region (SFR) and the matting face region. Main results. These strategies significantly enhanced recognition performance compared to directly lowering resolution (DLR) with Gaussian dots. The inclusion of certain external features, such as hairstyle, was beneficial for face recognition. Given the high recognition accuracy achieved and applicable processing speed, SFR-ROI was the preferred strategy. DLR processing resulted in significant face gender recognition differences (i.e. females were more easily recognized than males), but these differences were not apparent with other strategies. Significance. Face detection-based image processing strategies improved visual perception by highlighting useful information. Their use is advisable for face recognition when using low-resolution prosthetic vision. These results provide information for the continued design of image processing modules for use in visual prosthetics, thus maximizing the benefits for future prosthesis wearers.

  15. Face recognition in simulated prosthetic vision: face detection-based image processing strategies.

    Science.gov (United States)

    Wang, Jing; Wu, Xiaobei; Lu, Yanyu; Wu, Hao; Kan, Han; Chai, Xinyu

    2014-08-01

    Given the limited visual percepts elicited by current prosthetic devices, it is essential to optimize image content in order to assist implant wearers to achieve better performance of visual tasks. This study focuses on recognition of familiar faces using simulated prosthetic vision. Combined with region-of-interest (ROI) magnification, three face extraction strategies based on a face detection technique were used: the Viola-Jones face region, the statistical face region (SFR) and the matting face region. These strategies significantly enhanced recognition performance compared to directly lowering resolution (DLR) with Gaussian dots. The inclusion of certain external features, such as hairstyle, was beneficial for face recognition. Given the high recognition accuracy achieved and applicable processing speed, SFR-ROI was the preferred strategy. DLR processing resulted in significant face gender recognition differences (i.e. females were more easily recognized than males), but these differences were not apparent with other strategies. Face detection-based image processing strategies improved visual perception by highlighting useful information. Their use is advisable for face recognition when using low-resolution prosthetic vision. These results provide information for the continued design of image processing modules for use in visual prosthetics, thus maximizing the benefits for future prosthesis wearers.

  16. Application of neural classifier to risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography

    Science.gov (United States)

    Wydrzyński, Jacek; Jankowski, Stanisław; Piątkowska-Janko, Ewa

    2008-01-01

    This paper presents the application of neural networks to the risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution electrocardiography. This work is based on dataset obtained from the Medical University of Warsaw. The studies were performed on one multiclass classifier and on binary classifiers. For each case the optimal number of hidden neurons was found. The effect of data preparation: normalization and the proper selection of parameters was considered, as well as the influence of applied filters. The best neural classifier contains 5 hidden neurons, the input ECG signal is represented by 8 parameters. The neural network classifier had high rate of successful recognitions up to 90% performed on the test data set.

  17. A High-Performance Neural Prosthesis Enabled by Control Algorithm Design

    Science.gov (United States)

    Gilja, Vikash; Nuyujukian, Paul; Chestek, Cindy A.; Cunningham, John P.; Yu, Byron M.; Fan, Joline M.; Churchland, Mark M.; Kaufman, Matthew T.; Kao, Jonathan C.; Ryu, Stephen I.; Shenoy, Krishna V.

    2012-01-01

    Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer disabled patients greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower with less accurate control than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF), that incorporates assumptions about the nature of closed loop neural prosthetic control. When tested with rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperforms existing neural prostheses in all measured domains and halves acquisition time. This control algorithm permits sustained uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation across two monkeys, thereby increasing the clinical viability of neural prostheses. PMID:23160043

  18. Control of Prosthetic Hands via the Peripheral Nervous System

    National Research Council Canada - National Science Library

    Ciancio, Anna Lisa; Cordella, Francesca; Barone, Roberto; Romeo, Rocco Antonio; Bellingegni, Alberto Dellacasa; Sacchetti, Rinaldo; Davalli, Angelo; Di Pino, Giovanni; Ranieri, Federico; Di Lazzaro, Vincenzo; Guglielmelli, Eugenio; Zollo, Loredana

    2016-01-01

    ...), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands...

  19. Design of a prosthetic hand with remote actuation.

    Science.gov (United States)

    Scott, Kurt; Perez-Gracia, Alba

    2012-01-01

    One of the main issues of prosthetic hands is to be able to fulfill all the specifications about speed, torque, weight and inertia while placing all the components within the prosthetic hand. This is especially true when full dexterity is required in the prosthesis. In this paper, a new design for a prosthetic hand is presented, which uses remote actuation in order to satisfy most of those requirements. The actuators are to be located in the back of the subject and the transmission is implemented via cables. Other characteristics of this new prosthetic hand include torque limitation and the possibility of switching between underactuated and fully actuated functions.

  20. Factors Associated with Prosthetic Looseness in Lower Limb Amputees.

    Science.gov (United States)

    Phonghanyudh, Thong; Sutpasanon, Taweesak; Hathaiareerug, Chanasak; Devakula, M L Buddhibongsa; Kumnerddee, Wipoo

    2015-12-01

    To determine the factors associated with prosthetic looseness in lower limb amputees in Sisaket province. The present was a cross-sectional descriptive study. Subjects were lower limb amputees who previously obtained prostheses and required prosthetic replacements at the mobile prosthetic laboratory unit under the Prostheses Foundation of H.R.H. the Princess Mother at Khun Han Hospital, Sisaket province, in February 2013. Data including participant characteristics, prosthetic looseness data, and various variables were collected by direct semi-structured interview. Energy expenditures in physical activities were measured using the Thai version of the short format international physical activity questionnaire. Data between participants with and without prosthetic looseness were compared to determine prosthetic loosening associated factors. Among 101 participants enrolled, 33 (32.7%) had prosthetic looseness with average onset of 1.76 ± 1.67 years. Diabetes mellitus was the only significant factor associated with prosthetic looseness from both univariate and multivariate analyses (HR = 7.05, p = 0.002 and HR = 5.93, p = 0.007 respectively). Among the lower limb amputees in Sisaket province, diabetes mellitus was the only factor associated with prosthetic looseness. Therefore, diabetic screening should be supplemented in lower limb amputee assessment protocol. In addition, we recommend that amputees with diabetes mellitus should receive prosthesis check out at approximately

  1. The role of osteoblasts in peri-prosthetic osteolysis.

    LENUS (Irish Health Repository)

    O'Neill, S C

    2013-08-01

    Peri-prosthetic osteolysis and subsequent aseptic loosening is the most common reason for revising total hip replacements. Wear particles originating from the prosthetic components interact with multiple cell types in the peri-prosthetic region resulting in an inflammatory process that ultimately leads to peri-prosthetic bone loss. These cells include macrophages, osteoclasts, osteoblasts and fibroblasts. The majority of research in peri-prosthetic osteolysis has concentrated on the role played by osteoclasts and macrophages. The purpose of this review is to assess the role of the osteoblast in peri-prosthetic osteolysis. In peri-prosthetic osteolysis, wear particles may affect osteoblasts and contribute to the osteolytic process by two mechanisms. First, particles and metallic ions have been shown to inhibit the osteoblast in terms of its ability to secrete mineralised bone matrix, by reducing calcium deposition, alkaline phosphatase activity and its ability to proliferate. Secondly, particles and metallic ions have been shown to stimulate osteoblasts to produce pro inflammatory mediators in vitro. In vivo, these mediators have the potential to attract pro-inflammatory cells to the peri-prosthetic area and stimulate osteoclasts to absorb bone. Further research is needed to fully define the role of the osteoblast in peri-prosthetic osteolysis and to explore its potential role as a therapeutic target in this condition.

  2. Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle.

    Science.gov (United States)

    Xu, Bin; Yang, Chenguang; Pan, Yongping

    2015-10-01

    This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

  3. Artificial neural network application for space station power system fault diagnosis

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  4. Transfer functions for protein signal transduction: application to a model of striatal neural plasticity.

    Directory of Open Access Journals (Sweden)

    Gabriele Scheler

    Full Text Available We present a novel formulation for biochemical reaction networks in the context of protein signal transduction. The model consists of input-output transfer functions, which are derived from differential equations, using stable equilibria. We select a set of "source" species, which are interpreted as input signals. Signals are transmitted to all other species in the system (the "target" species with a specific delay and with a specific transmission strength. The delay is computed as the maximal reaction time until a stable equilibrium for the target species is reached, in the context of all other reactions in the system. The transmission strength is the concentration change of the target species. The computed input-output transfer functions can be stored in a matrix, fitted with parameters, and even recalled to build dynamical models on the basis of state changes. By separating the temporal and the magnitudinal domain we can greatly simplify the computational model, circumventing typical problems of complex dynamical systems. The transfer function transformation of biochemical reaction systems can be applied to mass-action kinetic models of signal transduction. The paper shows that this approach yields significant novel insights while remaining a fully testable and executable dynamical model for signal transduction. In particular we can deconstruct the complex system into local transfer functions between individual species. As an example, we examine modularity and signal integration using a published model of striatal neural plasticity. The modularizations that emerge correspond to a known biological distinction between calcium-dependent and cAMP-dependent pathways. Remarkably, we found that overall interconnectedness depends on the magnitude of inputs, with higher connectivity at low input concentrations and significant modularization at moderate to high input concentrations. This general result, which directly follows from the properties of

  5. Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery

    Directory of Open Access Journals (Sweden)

    Jeong-In Hwang

    2017-09-01

    Full Text Available For ship detection, X-band synthetic aperture radar (SAR imagery provides very useful data, in that ship targets look much brighter than surrounding sea clutter due to the corner-reflection effect. However, there are many phenomena which bring out false detection in the SAR image, such as noise of background, ghost phenomena, side-lobe effects and so on. Therefore, when ship-detection algorithms are carried out, we should consider these effects and mitigate them to acquire a better result. In this paper, we propose an efficient method to detect ship targets from X-band Kompsat-5 SAR imagery using the artificial neural network (ANN. The method produces the ship-probability map using ANN, and then detects ships from the ship-probability map by using a threshold value. For the purpose of getting an improved ship detection, we strived to produce optimal input layers used for ANN. In order to reduce phenomena related to the false detections, the non-local (NL-means filter and median filter were utilized. The NL-means filter effectively reduced noise on SAR imagery without smoothing edges of the objects, and the median filter was used to remove ship targets in SAR imagery. Through the filtering approaches, we generated two input layers from a Kompsat-5 SAR image, and created a ship-probability map via ANN from the two input layers. When the threshold value of 0.67 was imposed on the ship-probability map, the result of ship detection from the ship-probability map was a 93.9% recall, 98.7% precision and 6.1% false alarm rate. Therefore, the proposed method was successfully applied to the ship detection from the Kompsat-5 SAR image.

  6. Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

    Directory of Open Access Journals (Sweden)

    Abdullahi Abubakar Mas’ud

    2016-07-01

    Full Text Available In order to investigate how artificial neural networks (ANNs have been applied for partial discharge (PD pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1 determining the optimum weights in training the ANN; (2 using PD data captured over long stressing period in training the ANN; (3 ANN recognizing different PD degradation levels; (4 using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5 understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6 developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

  7. Combined application of Artificial Neural Networks and life cycle assessment in lentil farming in Iran

    Directory of Open Access Journals (Sweden)

    Behzad Elhami

    2017-03-01

    Full Text Available In this study, an Artificial Neural Network (ANN was applied to model yield and environmental emissions from lentil cultivation in Esfahan province of Iran. Data was gathered from lentil farmers using face to face questionnaire method during 2014–2015 cropping season. Life cycle assessment (LCA was applied to investigate the environmental impact categories associated with lentil production. Based on the results, total energy input, energy output to input ratio and energy productivity were determined to be 32,970.10 MJ ha−1, 0.902 and 0.06 kg MJ−1, respectively. The greatest amount of energy consumption was attributed to chemical fertilizer (42.76%. Environmental analysis indicated that the acidification potential was higher than other environmental impact categories in lentil production system. Also results showed that the production of agricultural machinery was the main hotspot in abiotic depletion, eutrophication, global warming, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity and terrestrial ecotoxicity impact categories, while direct emissions associated with lentil cultivation was the main hotspot in acidification potential and photochemical oxidation potential. In addition, diesel fuel was the main hotspot only in ozone layer depletion. The ANN model with 9-10-6-11 structure was identified as the most appropriate network for predicting yield and related environmental impact categories of lentil cultivation. Overall, the results of sensitivity analysis revealed that farmyard manure had the greatest effect on the most of the environmental impacts, while machinery was the most affecting parameter on the yield of the crop.

  8. Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

    Science.gov (United States)

    Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

    2015-09-01

    In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Reconsidering evidence-based practice in prosthetic rehabilitation : a shared enterprise

    NARCIS (Netherlands)

    van Twillert, S.; Geertzen, J.; Hemminga, T.; Postema, K.; Lettinga, A.

    Background: A divide is experienced between producers and users of evidence in prosthetic rehabilitation. Objective: To discuss the complexity inherent in establishing evidence-based practice in a prosthetic rehabilitation team illustrated by the case of prosthetic prescription for elderly

  10. Polypyrrole/Alginate Hybrid Hydrogels: Electrically Conductive and Soft Biomaterials for Human Mesenchymal Stem Cell Culture and Potential Neural Tissue Engineering Applications.

    Science.gov (United States)

    Yang, Sumi; Jang, LindyK; Kim, Semin; Yang, Jongcheol; Yang, Kisuk; Cho, Seung-Woo; Lee, Jae Young

    2016-11-01

    Electrically conductive biomaterials that can efficiently deliver electrical signals to cells or improve electrical communication among cells have received considerable attention for potential tissue engineering applications. Conductive hydrogels are desirable particularly for neural applications, as they can provide electrical signals and soft microenvironments that can mimic native nerve tissues. In this study, conductive and soft polypyrrole/alginate (PPy/Alg) hydrogels are developed by chemically polymerizing PPy within ionically cross-linked alginate hydrogel networks. The synthesized hydrogels exhibit a Young's modulus of 20-200 kPa. Electrical conductance of the PPy/Alg hydrogels could be enhanced by more than one order of magnitude compared to that of pristine alginate hydrogels. In vitro studies with human bone marrow-derived mesenchymal stem cells (hMSCs) reveal that cell adhesion and growth are promoted on the PPy/Alg hydrogels. Additionally, the PPy/Alg hydrogels support and greatly enhance the expression of neural differentiation markers (i.e., Tuj1 and MAP2) of hMSCs compared to tissue culture plate controls. Subcutaneous implantation of the hydrogels for eight weeks induces mild inflammatory reactions. These soft and conductive hydrogels will serve as a useful platform to study the effects of electrical and mechanical signals on stem cells and/or neural cells and to develop multifunctional neural tissue engineering scaffolds. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Design and evaluation of two different finger concepts for body-powered prosthetic hand.

    Science.gov (United States)

    Smit, Gerwin; Plettenburg, Dick H; van der Helm, Frans C T

    2013-01-01

    The goal of this study was to find an efficient method of energy transmission for application in an anthropomorphic underactuated body-powered (BP) prosthetic hand. A pulley-cable finger and a hydraulic cylinder finger were designed and tested to compare the pulley-cable transmission principle with the hydraulic cylinder transmission principle. Both fingers had identical dimensions and a low mass. The only thing that differed between the fingers was the transmission principle. The input energy was measured for a number of tasks. The pulley-cable finger required more input energy than the hydraulic cylinder finger to perform the tasks. This was especially the case in tasks that required high pinch forces. The hydraulic cylinder transmission is therefore the more efficient transmission for application in BP prosthetic fingers.

  12. Tactile sensing means for prosthetic limbs

    Science.gov (United States)

    Scott, W. L. (Inventor)

    1973-01-01

    An improved prosthetic device characterized by a frame and a socket for mounting on the stump of a truncated human appendage is described. Flexible digits extend from the distal end and transducers located within the digits act as sensing devices for detecting tactile stimuli. The transducers are connected through a power circuit with a slave unit supported by a strap and fixed to the stump. The tactile stimuli detected at the sensing devices are reproduced and applied to the skin of the appendage in order to stimulate the sensory organs located therein.

  13. Smart Prosthetic Hand Technology - Phase 2

    Science.gov (United States)

    2011-05-01

    Belgrade/USC Hand (Bekey, Tomovic and Zeljkovic 1990) has small conductive plastic potentiometers compact (35x10x3mm) with good resolution (320ohms...upper limb loss and their reported research priorities. Journal of Prosthetic and Orthotics 8 (1), 2–11. Bekey, G. A., Tomovic , R., Zeljkovic, I...G.R. Tomovic , and I. Zeljkovic. "Control Architecture for the Belgrade/USC Hand." In Dextrous Robot Hands, by S.T. Venkataram and T. Iberall, 136-149

  14. Injectible bodily prosthetics employing methacrylic copolymer gels

    Energy Technology Data Exchange (ETDEWEB)

    Mallapragada, Surya K.; Anderson, Brian C.

    2007-02-27

    The present invention provides novel block copolymers as structural supplements for injectible bodily prosthetics employed in medical or cosmetic procedures. The invention also includes the use of such block copolymers as nucleus pulposus replacement materials for the treatment of degenerative disc disorders and spinal injuries. The copolymers are constructed by polymerization of a tertiary amine methacrylate with either a (poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) polymer, such as the commercially available Pluronic.RTM. polymers, or a poly(ethylene glycol) methyl ether polymer.

  15. Closed Form Integration of Artificial Neural Networks with Some Applications to Finance

    OpenAIRE

    Gottschling, Andreas; Haefke, Christian; White, Halbert

    1999-01-01

    Many economic and econometric applications require the integration of functions lacking a closed form antiderivative, which is therefore a task that can only be solved by numerical methods. We propose a new family of probability densities that can be used as substitutes and have the property of closed form integrability. This is especially advantageous in cases where either the complexity of a problem makes numerical function evaluations very costly, or fast information extraction is required...

  16. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  17. Application of neural networks to obtain the site response in Mexico City

    Directory of Open Access Journals (Sweden)

    Vargas J. Carlos A.

    2003-08-01

    Full Text Available

    We have implemented a neural network of three hidden layers with 40 neurons each layer to be used as soil/rock transfer functions for two stations in Mexico City. The net was trained with supervised learning through input and output vectors of accelerations (twelve records, from five seismic events from Guerrero and Puebla, 5.8 M 7.3, and tested with three records not taken in account in the training. The results in the frequency domain are good, finding a seismic amplification between 0.2 to 5 Hz for the Lake zone. In the time domain we obtain results that are not coincident. Due to the data and the complex of the phenomena, it is necessary to apply this tool using more records for the training net, so the phenomena can be learned better through reliable database.

    Hemos implementado una red neuronal de tres capas escondidas con 40 neuronas por capa para ser usada como funciones de trasferencia suelo/roca en dos estaciones acelerométricas en Ciudad de México. La red fue entrenada con entrenamiento supervisado por medio de vectores de aceleración de entrada y salida (doce registros de cinco eventos sísmicos localizados en la costa de Guerrero y uno al sur de Puebla, 5, 8 M 7, 3, y probada con tres registros no tornados en cuenta en el entrenamiento de la red. Los resultados obtenidos en el dominio de la frecuencia son bastante buenos, encontrándose una amplificación sísmica entre 0,2 a 5 Hz para la zona de Lago (estación RMCS. En el dominio del tiempo obtuvimos resultados que no son coincidentes.

    The effect of vertical expandable prosthetic titanium rib on growth in congenital scoliosis

    OpenAIRE

    Mehmet Bulent Balioglu; Akif Albayrak; Yunus Emre Akman; Yunus Atici; Deniz Kargin; Mehmet Akif Kaygusuz

    2015-01-01

    Aims: In the treatment of thoracic insufficiency syndrome, the main aim is to maintain spinal and thoracic growth in order to continue respiratory functions. Vertical expandable prosthetic titanium rib (VEPTR) device application is a method of choice especially in the congenital cases with a thoracic deformity. In our study, we evaluated the effect of VEPTR on growth in congenital scoliosis. Materials and Methods: Four female patients in whom VEPTR was applied were retrospectively evaluated. ...

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

    Directory of Open Access Journals (Sweden)

    Yongcheng Li

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  1. A new fuzzy regression model based on interval-valued fuzzy neural network and its applications to management

    Directory of Open Access Journals (Sweden)

    Somaye Yeylaghi

    2017-06-01

    Full Text Available In this paper, a novel hybrid method based on interval-valued fuzzy neural network for approximate of interval-valued fuzzy regression models, is presented. The work of this paper is an expansion of the research of real fuzzy regression models. In this paper interval-valued fuzzy neural network (IVFNN can be trained with crisp and interval-valued fuzzy data. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples and compare this method with existing methods.

  2. Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images.

    Science.gov (United States)

    van Grinsven, Mark J J P; van Ginneken, Bram; Hoyng, Carel B; Theelen, Thomas; Sanchez, Clara I

    2016-05-01

    Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

  3. An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations.

    Science.gov (United States)

    Chronopoulos, Kostas I; Tsiros, Ioannis X; Dimopoulos, Ioannis F; Alvertos, Nikolaos

    2008-12-01

    In this work artificial neural network (ANN) models are developed to estimate meteorological data values in areas with sparse meteorological stations. A more traditional interpolation model (multiple regression model, MLR) is also used to compare model results and performance. The application site is a canyon in a National Forest located in southern Greece. Four meteorological stations were established in the canyon; the models were then applied to estimate air temperature values as a function of the corresponding values of one or more reference stations. The evaluation of the ANN model results showed that fair to very good air temperature estimations may be achieved depending on the number of the meteorological stations used as reference stations. In addition, the ANN model was found to have better performance than the MLR model: mean absolute error values were found to be in the range 0.82-1.72 degrees C and 0.90-1.81 degrees C, for the ANN and the MLR models, respectively. These results indicate that ANN models may provide advantages over more traditional models or methods for temperature and other data estimations in areas where meteorological stations are sparse; they may be adopted, therefore, as an important component in various environmental modeling and management studies.

  4. Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

    Science.gov (United States)

    Valdés, Julio J; Bonham-Carter, Graeme

    2006-03-01

    A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

  5. Application of a deep-learning method to the forecast of daily solar flare occurrence using Convolution Neural Network

    Science.gov (United States)

    Shin, Seulki; Moon, Yong-Jae; Chu, Hyoungseok

    2017-08-01

    As the application of deep-learning methods has been succeeded in various fields, they have a high potential to be applied to space weather forecasting. Convolutional neural network, one of deep learning methods, is specialized in image recognition. In this study, we apply the AlexNet architecture, which is a winner of Imagenet Large Scale Virtual Recognition Challenge (ILSVRC) 2012, to the forecast of daily solar flare occurrence using the MatConvNet software of MATLAB. Our input images are SOHO/MDI, EIT 195Å, and 304Å from January 1996 to December 2010, and output ones are yes or no of flare occurrence. We select training dataset from Jan 1996 to Dec 2000 and from Jan 2003 to Dec 2008. Testing dataset is chosen from Jan 2001 to Dec 2002 and from Jan 2009 to Dec 2010 in order to consider the solar cycle effect. In training dataset, we randomly select one fifth of training data for validation dataset to avoid the overfitting problem. Our model successfully forecasts the flare occurrence with about 0.90 probability of detection (POD) for common flares (C-, M-, and X-class). While POD of major flares (M- and X-class) forecasting is 0.96, false alarm rate (FAR) also scores relatively high(0.60). We also present several statistical parameters such as critical success index (CSI) and true skill statistics (TSS). Our model can immediately be applied to automatic forecasting service when image data are available.

  6. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. An Application of Artificial Neural Network to Compute the Resonant Frequency of E-Shaped Compact Microstrip Antennas

    Science.gov (United States)

    Akdagli, Ali; Toktas, Abdurrahim; Kayabasi, Ahmet; Develi, Ibrahim

    2013-09-01

    An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

  8. 38 CFR 17.150 - Prosthetic and similar appliances.

    Science.gov (United States)

    2010-07-01

    ... appliances. 17.150 Section 17.150 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS MEDICAL Prosthetic, Sensory, and Rehabilitative Aids § 17.150 Prosthetic and similar appliances. Artificial limbs, braces, orthopedic shoes, hearing aids, wheelchairs, medical accessories, similar...

  9. Prosthetic Frequently Asked Questions for the New Amputee

    Science.gov (United States)

    ... it with your favorite color or pattern. The prosthesis is an extension of you and your style – wear it proudly! Technology continues to change the prosthetic market. With advances in the microprocessor knee and foot, and advanced hands and sockets, prosthetics ...

  10. Prosthetic Rehabilitation in Children: An Alternative Clinical Technique

    Directory of Open Access Journals (Sweden)

    Nádia Carolina Teixeira Marques

    2013-01-01

    Full Text Available Complete and partial removable dentures have been used successfully in numerous patients with oligodontia and/or anodontia. However, there is little information in the literature regarding the principles and guidelines to prosthetic rehabilitation for growing children. This case report describes the management of a young child with oligodontia as well as the treatment planning and the prosthetic rehabilitation technique.

  11. Principles of obstacle avoidance with a transfemoral prosthetic limb

    NARCIS (Netherlands)

    van Keeken, Helco G.; Vrieling, Aline H.; Hof, At L.; Postema, Klaas; Otten, Bert

    2012-01-01

    In this study, conditions that enable a prosthetic knee flexion strategy in transfemoral amputee subjects during obstacle avoidance were investigated. This study explored the hip torque principle and the static ground principle as object avoidance strategies. A prosthetic limb simulator device was

  12. Stiffness and hysteresis properties of some prosthetic feet

    NARCIS (Netherlands)

    van Jaarsveld, H.W.L.; Grootenboer, H.J.; de Vries, J.; Koopman, Hubertus F.J.M.

    1990-01-01

    A prosthetic foot is an important element of a prosthesis, although it is not always fully recognized that the properties of the foot, along with the prosthetic knee joint and the socket, are in part responsible for the stability and metabolic energy cost during walking. The stiffness and the

  13. Redes neurais e suas aplicações em calibração multivariada Neural networks and its applications in multivariate calibration

    Directory of Open Access Journals (Sweden)

    Eduardo O. de Cerqueira

    2001-12-01

    Full Text Available Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

  14. Prosthetic implantation of the human vestibular system.

    Science.gov (United States)

    Golub, Justin S; Ling, Leo; Nie, Kaibao; Nowack, Amy; Shepherd, Sarah J; Bierer, Steven M; Jameyson, Elyse; Kaneko, Chris R S; Phillips, James O; Rubinstein, Jay T

    2014-01-01

    A functional vestibular prosthesis can be implanted in human such that electrical stimulation of each semicircular canal produces canal-specific eye movements while preserving vestibular and auditory function. A number of vestibular disorders could be treated with prosthetic stimulation of the vestibular end organs. We have previously demonstrated in rhesus monkeys that a vestibular neurostimulator, based on the Nucleus Freedom cochlear implant, can produce canal-specific electrically evoked eye movements while preserving auditory and vestibular function. An investigational device exemption has been obtained from the FDA to study the feasibility of treating uncontrolled Ménière's disease with the device. The UW/Nucleus vestibular implant was implanted in the perilymphatic space adjacent to the three semicircular canal ampullae of a human subject with uncontrolled Ménière's disease. Preoperative and postoperative vestibular and auditory function was assessed. Electrically evoked eye movements were measured at 2 time points postoperatively. Implantation of all semicircular canals was technically feasible. Horizontal canal and auditory function were largely, but not totally, lost. Electrode stimulation in 2 of 3 canals resulted in canal-appropriate eye movements. Over time, stimulation thresholds increased. Prosthetic implantation of the semicircular canals in humans is technically feasible. Electrical stimulation resulted in canal-specific eye movements, although thresholds increased over time. Preservation of native auditory and vestibular function, previously observed in animals, was not demonstrated in a single subject with advanced Ménière's disease.

  15. Is the prosthetic homologue necessary for embodiment?

    Directory of Open Access Journals (Sweden)

    Chelsea Dornfeld

    2016-12-01

    Full Text Available Embodiment is the process by which patients with limb loss come to accept their peripheral device as a natural extension of self. However, there is little guidance as to how exacting the prosthesis must be in order for embodiment to take place: is it necessary for the prosthetic hand to look just like the absent hand? Here, we describe a protocol for testing whether an individual would select a hand that looks like their own from among a selection of 5 hands, and whether the hand selection (regardless of homology is consistent across multiple exposures to the same (but reordered set of candidate hands. Pilot results using healthy volunteers reveals that hand selection is only modestly consistent, and that selection of the prosthetic homologue is atypical (61 of 192 total exposures. Our protocol can be executed in minutes, and makes use of readily available equipment and softwares. We present both a face-to-face and a virtual protocol, for maximum flexibility of implementation.

  16. The use of underactuation in prosthetic grasping

    Directory of Open Access Journals (Sweden)

    P. J. Kyberd

    2011-02-01

    Full Text Available Underactuation as a method of driving prosthetic hands has a long history. The pragmatic requirements of such a device to be light enough to be worn and used regularly have meant that any multi degree of freedom prosthetic hand must have fewer actuators than the usable degrees of freedom. Aesthetics ensures that while the hand needs five fingers, five actuators have considerable mass, and only in recent years has it even been possible to construct a practical anthropomorphic hand with five motors. Thus there is an important trade off as to which fingers are driven, and which joints on which fingers are actuated, and how the forces are distributed to create a functional device. This paper outlines some of the historical solutions created for this problem and includes those designs of recent years that are now beginning to be used in the commercial environment.

    This paper was presented at the IFToMM/ASME International Workshop on Underactuated Grasping (UG2010, 19 August 2010, Montréal, Canada.

  17. The Prosthetic Workflow in the Digital Era

    Directory of Open Access Journals (Sweden)

    Lidia Tordiglione

    2016-01-01

    Full Text Available The purpose of this retrospective study was to clinically evaluate the benefits of adopting a full digital workflow for the implementation of fixed prosthetic restorations on natural teeth. To evaluate the effectiveness of these protocols, treatment plans were drawn up for 15 patients requiring rehabilitation of one or more natural teeth. All the dental impressions were taken using a Planmeca PlanScan® (Planmeca OY, Helsinki, Finland intraoral scanner, which provided digital casts on which the restorations were digitally designed using Exocad® (Exocad GmbH, Germany, 2010 software and fabricated by CAM processing on 5-axis milling machines. A total of 28 single crowns were made from monolithic zirconia, 12 vestibular veneers from lithium disilicate, and 4 three-quarter vestibular veneers with palatal extension. While the restorations were applied, the authors could clinically appreciate the excellent match between the digitally produced prosthetic design and the cemented prostheses, which never required any occlusal or proximal adjustment. Out of all the restorations applied, only one exhibited premature failure and was replaced with no other complications or need for further scanning. From the clinical experience gained using a full digital workflow, the authors can confirm that these work processes enable the fabrication of clinically reliable restorations, with all the benefits that digital methods bring to the dentist, the dental laboratory, and the patient.

  18. The Prosthetic Workflow in the Digital Era.

    Science.gov (United States)

    Tordiglione, Lidia; De Franco, Michele; Bosetti, Giovanni

    2016-01-01

    The purpose of this retrospective study was to clinically evaluate the benefits of adopting a full digital workflow for the implementation of fixed prosthetic restorations on natural teeth. To evaluate the effectiveness of these protocols, treatment plans were drawn up for 15 patients requiring rehabilitation of one or more natural teeth. All the dental impressions were taken using a Planmeca PlanScan® (Planmeca OY, Helsinki, Finland) intraoral scanner, which provided digital casts on which the restorations were digitally designed using Exocad® (Exocad GmbH, Germany, 2010) software and fabricated by CAM processing on 5-axis milling machines. A total of 28 single crowns were made from monolithic zirconia, 12 vestibular veneers from lithium disilicate, and 4 three-quarter vestibular veneers with palatal extension. While the restorations were applied, the authors could clinically appreciate the excellent match between the digitally produced prosthetic design and the cemented prostheses, which never required any occlusal or proximal adjustment. Out of all the restorations applied, only one exhibited premature failure and was replaced with no other complications or need for further scanning. From the clinical experience gained using a full digital workflow, the authors can confirm that these work processes enable the fabrication of clinically reliable restorations, with all the benefits that digital methods bring to the dentist, the dental laboratory, and the patient.

  19. Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

    Science.gov (United States)

    Allison, Thomas C

    2016-03-03

    Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 (•)OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which (•)OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.

  20. Weed management through herbicide application in direct-seeded rice and yield modeling by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Ghosh, D.; Singh, U.P.; Ray, K.; Das, A.

    2016-11-01

    In direct seeded rice (DSR) cultivation, weed is the major constraint mainly due to absence of puddling in field. The yield loss due to weed interference is huge, may be up to 100%. In this perspective, the present experiment was conducted to study the efficacy of selected herbicides, and to predict the rice yield using artificial neural network (ANN) models. The dry weight and density of weeds were recorded at different growth stages and consequently herbicidal efficacy was evaluated. Experimental results revealed that pre-emergence (PRE) herbicide effectively controlled the germination of grassy weeds. Application bispyribac-sodium as post-emergence (POST) following PRE herbicides (clomazone or pendimethalin) or as tank-mixture with clomazone effectively reduced the density and biomass accumulation of diverse weed flora in DSR. Herbicidal treatments improved the plant height, yield attributes and grain yield (2.7 to 5.5 times) over weedy check. The sensitivity of the best ANN model clearly depicts that the weed control index (WCI) of herbicides was most important than their weed control efficiency (WCE). Besides, the early control of weeds is a better prescription to improve rice yield. Differences in sensitivity values of WCI and WCE across the crop growth stages also suggest that at 15, 30 and 60 days after sowing, herbicides most effectively controlled sedges, broad leaves and grasses, respectively. Based on the grain yield and herbicidal WCE, it can be concluded that the combined application of pendimethalin or clomazone as PRE followed by bispyribac-sodium as POST or tank-mixture of clomazone + bispyribac sodium can effectively control different weed flushes throughout the crop growth period in DSR. (Author)