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PubMed2003

An intelligent controller for automated operation of sequencing batch reactors.

In this paper the results are presented of original research into the automatic and "intelligent" detection of breakpoints in Dissolved Oxygen (DO) profiles. The research has been based on a large body of data collected from laboratory SBRs operating on synthetic wastewater. Two different approaches were followed to identify the endpoints. The paper analyses and evaluates the results of automatic detection on the basis of geometric features in the DO profiles. This was followed by classification of the detected breakpoints using different soft computing techniques based on Neural Network (NN), Fuzzy Neural Network (FuNN) and Evolving Fuzzy Neural Network (EfuNN) software systems for breakpoint classification. A high rate of successful detection and classification was obtained with up to 96% of the decisions made correctly. In order to overcome the limitations of this system to adapt to dynamically changing process conditions, an intelligent control model was developed by a combination between an Evolving Fuzzy Neural Net (EfuNN) combined with a logic decision unit. This system has the ability to "learn on-the-fly" and adjust its response pattern in order to maintain a high rate of successful breakpoint detection under varying changing process conditions. This software system has been sucessfully embedded on a small programmable controller for integration into larger process control systems for the operation of SBR plants.

Automation↗
PubMedDate not supplied

[Neurocomputing--the reality and the prospects. The prerequisites for the emergence of neurocomputing as a new computer technology].

Summary of the history of beginning and contemporary state of neurocomputing is given as a new trend of computing techniques. Peculiarities of architecture and functioning of neurocomputers are discussed in comparison with the ordinary computers. Perspectives and fields of application of neurocomputers are analyzed. Measures are proposed concerning the development and introduction of neurocomputing in the USSR.

Academies and Institutes↗
PubMed1997

Evaluation of PAPNET testing as an ancillary tool to clarify the status of the "atypical" cervical smear.

To assess the utility of the PAPNET system (Neuromedical Systems Inc., Suffern, NY) in clarifying the status of cervical smears showing borderline abnormalities, we analyzed the results of five cytotechnologists who reclassified 200 "atypical" smears by evaluating PAPNET images only. The interobserver agreement (reliability) of the PAPNET reviewers was computed, and their readings were compared with three standards: the consensus diagnosis of five pathologists who used light microscopy, the detection of cancer-associated human papillomavirus DNA by Southern analysis, and the correlation with diagnoses of biopsy specimens obtained during passive follow-up. The PAPNET reviewers classified 18 to 65% of cases as normal, 25 to 42% as equivocal, and 10 to 55% as abnormal. Unanimous interobserver agreement was achieved in only 24 (13%) cases. Four of the five PAPNET reviewers agreed moderately well with the results of the pathology reference panel. In four of the five PAPNET reviews, classification of cases as abnormal was strongly correlated with the detection of cancer-associated types of human papillomavirus. Consensus PAPNET results of abnormal were predictive of abnormal histologic findings at follow-up. Theoretically, if colposcopy had been performed on all of the women with equivocal or abnormal PAPNET results (based on the consensus of the panel), as much as 95% of biopsy-confirmed lesions could have been detected, but 79% of women would have been referred. Restriction of colposcopy referral to women with definitely abnormal PAPNET readings would have reduced referrals to 31%, but the sensitivity of the triage would have dropped to 51% of biopsy-confirmed lesions.

Biopsy↗
PubMed1989

The recent excitement about neural networks.

The remarkable properties of some recent computer algorithms for neural networks seemed to promise a fresh approach to understanding the computational properties of the brain. Unfortunately most of these neural nets are unrealistic in important respects.

Algorithms↗
PubMed1995

Medical image processing utilizing neural networks trained on a massively parallel computer.

While finding many applications in science, engineering, and medicine, artificial neural networks (ANNs) have typically been limited to small architectures. In this paper, we demonstrate how very large architecture neural networks can be trained for medical image processing utilizing a massively parallel, single-instruction multiple data (SIMD) computer. The two- to three-orders of magnitude improvement in processing time attainable using a parallel computer makes it practical to train very large architecture ANNs. As an example we have trained several ANNs to demonstrate the tomographic reconstruction of 64 x 64 single photon emission computed tomography (SPECT) images from 64 planar views of the images. The potential for these large architecture ANNs lies in the fact that once the neural network is properly trained on the parallel computer the corresponding interconnection weight file can be loaded on a serial computer. Subsequently, relatively fast processing of all novel images can be performed on a PC or workstation.

Computer Systems↗
PubMed1994

Computer-assisted cervical cancer screening using neural networks.

A practical and effective system for the computer-assisted screening of conventionally prepared cervical smears is presented and described. Recent developments in neural network technology have made computerized analysis of the complex cellular scenes found on Pap smears possible. The PAPNET Cytological Screening System uses neural networks to automatically analyze conventional smears by locating and recognizing potentially abnormal cells. It then displays images of these objects for review and final diagnosis by qualified cytologists. The results of the studies presented indicate that the PAPNET system could be a useful tool for both the screening and rescreening of cervical smears. In addition, the system has been shown to be sensitive to some types of abnormalities which have gone undetected during manual screening.

Diagnosis, Computer-Assisted↗
PubMed1999

Analog circuits for modeling biological neural networks: design and applications.

Computational neuroscience is emerging as a new approach in biological neural networks studies. In an attempt to contribute to this field, we present here a modeling work based on the implementation of biological neurons using specific analog integrated circuits. We first describe the mathematical basis of such models, then present analog emulations of different neurons. Each model is compared to its biological real counterpart as well as its numerical computation. Finally, we demonstrate the possible use of these analog models to interact dynamically with real cells through artificial synapses within hybrid networks. This method is currently used to explore neural networks dynamics.

Animals↗
PubMed1996

Neuronal networks for induced '40 Hz' rhythms.

A fast, coherent EEG rhythm, called a gamma or a '40 Hz' rhythm, has been implicated both in higher brain functions, such as the 'binding' of features that are detected by sensory cortices into perceived objects, and in lower level processes, such as the phase coding of neuronal activity. Computer simulations of several parts of the brain suggest that gamma rhythms can be generated by pools of excitatory neurones, networks of inhibitory neurones, or networks of both excitatory and inhibitory neurones. The strongest experimental evidence for rhythm generators has been shown for: (1) neocortical and thalamic neurones that are intrinsic '40 Hz' oscillators, although synchrony still requires network mechanisms; and (2) hippocampal and neocortical networks of mutually inhibitory interneurones that generate collective 40 Hz rhythms when excited tonically.

Animals↗
PubMed1991

Computer recognition of brain stem auditory evoked potential wave V by a neural network.

A neural network simulator was used to create a connectionist model for the recognition of the peak of wave V of the brain stem auditory evoked potential (BAEP) test. Wave forms were selected from BAEPs performed in the last four years at the University of Pittsburgh Presbyterian University Hospital (PUH). The ipsilateral and contralateral wave forms were digitized and then sampled at 0.1 msec intervals using linear interpolation. The resulting amplitudes were normalized to the range less than -1, 1 greater than. The normalized amplitudes were used as the initial activation values for the processing elements of the input layer. The desired outputs (the target locations for wave V) were determined by adjusting the latencies recorded by the physician interpreter for any distortion in the digitizing process. The location of wave V was represented in the output layer by setting the output element which correspond to the target location and its immediate neighbors to high activation levels and all the remaining output units to zero activity. Two network architectures, differing only in the hidden unit layer, with 40 and 16 hidden units respectively, were used. The networks were trained using standard back-propagation. Several trials from different starting points were performed for each architecture. The training set was composed of the wave forms resulting from the stimulation of 50 ears. The best network, found after 60 epochs (3000 presentations) was able to correctly identify 17 out of 20 cases (85%) from a set of test cases which were independent from the training set.

Diagnosis, Computer-Assisted↗
PubMed2005

An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator.

Algorithms↗
PubMed1995

Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network.

We are developing a computer program for automated detection of clustered microcalcifications on mammograms. In this study, we investigated the effectiveness of a signal classifier based on a convolution neural network (CNN) approach for improvement of the accuracy of the detection program. Fifty-two mammograms with clustered microcalcifications were selected from patient files. The clusters on the mammograms were ranked by experienced mammographers and divided into an obvious group, an average group, and a subtle group. The average and subtle groups were combined and randomly divided into two sets, each of which was used as training or test set alternately. The obvious group served as an additional independent test set. Regions of interest (ROIs) containing potential individual microcalcifications were first located on each mammogram by the automated detection program. The ROIs from one set of the mammograms were used to train CNNs of different configurations with a back-propagation method. The generalization capability of the trained CNNs was then examined by their accuracy of classifying the ROIs from the other set and from the obvious group. The classification accuracy of the CNNs for the ROIs was evaluated by receiver operating characteristic (ROC) analysis. It was found that CNNs of many different configurations can reach approximately the same performance level, with the area under the ROC curve (Az) of 0.9. We incorporated a trained CNN into the detection program and evaluated the improvement of the detection accuracy by the CNN using free response ROC analysis. Our results indicated that, over a wide range of true-positive (TP) cluster detection rate, the CNN classifier could reduce the number of false-positive (FP) clusters per image by more than 70%. For the obvious cases, at a TP rate of 100%, the FP rate reduced from 0.35 cluster per image to 0.1 cluster per image. For the average and subtle cases, the detection accuracy improved from a TP rate of 87% at an FP rate of four clusters per image to a TP rate of 90% at an FP rate of 1.5 clusters per image.

Automation↗