A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

Science.gov (United States)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Using CNN Features to Better Understand What Makes Visual Artworks Special

Directory of Open Access Journals (Sweden)

Anselm Brachmann

2017-05-01

Full Text Available One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN, which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond (richness of feature responses. Results for the second measure imply that this diversity is tied to a relatively large variability of the responses of individual CNN feature across the subregions of an image. We hypothesize that this combination of richness and variability of CNN feature responses is one of properties that makes traditional visual artworks special. We discuss the possible neural underpinnings of this perceptual quality of artworks and propose to study the same quality also in other types of aesthetic stimuli, such as music and literature.

A CNN Based Approach for Garments Texture Design Classification

Directory of Open Access Journals (Sweden)

S.M. Sofiqul Islam

2017-05-01

Full Text Available Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet and some state-of-the-art Hand-Engineered feature extraction methods.

Real-time vehicle detection and tracking in video based on faster R-CNN

Science.gov (United States)

Zhang, Yongjie; Wang, Jian; Yang, Xin

2017-08-01

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

Science.gov (United States)

Zheng, Liang; Yang, Yi; Tian, Qi

2018-05-01

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

OpenAIRE

Kang, Kai; Li, Hongsheng; Yan, Junjie; Zeng, Xingyu; Yang, Bin; Xiao, Tong; Zhang, Cong; Wang, Zhe; Wang, Ruohui; Wang, Xiaogang; Ouyang, Wanli

2016-01-01

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet and VGG, novel object detection frameworks such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and context...

Centrioles regulate centrosome size by controlling the rate of Cnn incorporation into the PCM.

Science.gov (United States)

Conduit, Paul T; Brunk, Kathrin; Dobbelaere, Jeroen; Dix, Carly I; Lucas, Eliana P; Raff, Jordan W

2010-12-21

centrosomes are major microtubule organizing centers in animal cells, and they comprise a pair of centrioles surrounded by an amorphous pericentriolar material (PCM). Centrosome size is tightly regulated during the cell cycle, and it has recently been shown that the two centrosomes in certain stem cells are often asymmetric in size. There is compelling evidence that centrioles influence centrosome size, but how centrosome size is set remains mysterious. we show that the conserved Drosophila PCM protein Cnn exhibits an unusual dynamic behavior, because Cnn molecules only incorporate into the PCM closest to the centrioles and then spread outward through the rest of the PCM. Cnn incorporation into the PCM is driven by an interaction with the conserved centriolar proteins Asl (Cep152 in humans) and DSpd-2 (Cep192 in humans). The rate of Cnn incorporation into the PCM is tightly regulated during the cell cycle, and this rate influences the amount of Cnn in the PCM, which in turn is an important determinant of overall centrosome size. Intriguingly, daughter centrioles in syncytial embryos only start to incorporate Cnn as they disengage from their mothers; this generates a centrosome size asymmetry, with mother centrioles always initially organizing more Cnn than their daughters. centrioles can control the amount of PCM they organize by regulating the rate of Cnn incorporation into the PCM. This mechanism can explain how centrosome size is regulated during the cell cycle and also allows mother and daughter centrioles to set centrosome size independently of one another.

Small-size pedestrian detection in large scene based on fast R-CNN

Science.gov (United States)

Wang, Shengke; Yang, Na; Duan, Lianghua; Liu, Lu; Dong, Junyu

2018-04-01

Pedestrian detection is a canonical sub-problem of object detection with high demand during recent years. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for small size pedestrian detection in large-view scene. We study that the insufficient resolution of feature maps lead to the unsatisfactory accuracy when handling small instances. In this paper, we investigate issues involving Fast R-CNN for pedestrian detection. Driven by the observations, we propose a very simple but effective baseline for pedestrian detection based on Fast R-CNN, employing the DPM detector to generate proposals for accuracy, and training a fast R-CNN style network to jointly optimize small size pedestrian detection with skip connection concatenating feature from different layers to solving coarseness of feature maps. And the accuracy is improved in our research for small size pedestrian detection in the real large scene.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

Science.gov (United States)

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Some Congruence Properties of a Restricted Bipartition Function cN(n

Directory of Open Access Journals (Sweden)

Nipen Saikia

2016-01-01

Full Text Available Let cN(n denote the number of bipartitions (λ,μ of a positive integer n subject to the restriction that each part of μ is divisible by N. In this paper, we prove some congruence properties of the function cN(n for N=7, 11, and 5l, for any integer l≥1, by employing Ramanujan’s theta-function identities.

CNN Newsroom Classroom Guides, October 2000.

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Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Chinese authorities detain Falun Gong protesters on Tiananmen Square…

Inspecting rapidly moving surfaces for small defects using CNN cameras

Science.gov (United States)

Blug, Andreas; Carl, Daniel; Höfler, Heinrich

2013-04-01

A continuous increase in production speed and manufacturing precision raises a demand for the automated detection of small image features on rapidly moving surfaces. An example are wire drawing processes where kilometers of cylindrical metal surfaces moving with 10 m/s have to be inspected for defects such as scratches, dents, grooves, or chatter marks with a lateral size of 100 μm in real time. Up to now, complex eddy current systems are used for quality control instead of line cameras, because the ratio between lateral feature size and surface speed is limited by the data transport between camera and computer. This bottleneck is avoided by "cellular neural network" (CNN) cameras which enable image processing directly on the camera chip. This article reports results achieved with a demonstrator based on this novel analogue camera - computer system. The results show that computational speed and accuracy of the analogue computer system are sufficient to detect and discriminate the different types of defects. Area images with 176 x 144 pixels are acquired and evaluated in real time with frame rates of 4 to 10 kHz - depending on the number of defects to be detected. These frame rates correspond to equivalent line rates on line cameras between 360 and 880 kHz, a number far beyond the available features. Using the relation between lateral feature size and surface speed as a figure of merit, the CNN based system outperforms conventional image processing systems by an order of magnitude.

The CNN Effect: Stretegic Enabler or Operational Risk?

National Research Council Canada - National Science Library

Belknap, Margaret

2001-01-01

.... Satellite technology and the proliferation of 2417 news networks have created and increased the so-called 'CNN effect' on strategic level decision-making and how warfighters direct their commands...

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

Science.gov (United States)

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

CNN for breaking text-based CAPTCHA with noise

Science.gov (United States)

Liu, Kaixuan; Zhang, Rong; Qing, Ke

2017-07-01

A CAPTCHA ("Completely Automated Public Turing test to tell Computers and Human Apart") system is a program that most humans can pass but current computer programs could hardly pass. As the most common type of CAPTCHAs , text-based CAPTCHA has been widely used in different websites to defense network bots. In order to breaking textbased CAPTCHA, in this paper, two trained CNN models are connected for the segmentation and classification of CAPTCHA images. Then base on these two models, we apply sliding window segmentation and voting classification methods realize an end-to-end CAPTCHA breaking system with high success rate. The experiment results show that our method is robust and effective in breaking text-based CAPTCHA with noise.

New Digital Approach to CNN On-chip Implementation for Pattern Recognition

OpenAIRE

Durackova, Daniela

2008-01-01

We developed a novel simulator for the CNN using the program tool Visual Basic for Application. Its algorithm is based on the same principle as the planned designed circuit. The network can process the patterns with 400 point recognition. The created universal simulator can change various simulation parameters. We found that the rounding at multiplication is not as important as we previously expected. On the basis of the simulations we designed a novel digital CNN cell implemented on a chip. ...

A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

C. Zhang

2017-09-01

Full Text Available Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP, which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

Science.gov (United States)

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs

Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template

International Nuclear Information System (INIS)

Hirakawa, Satoshi; Nishio, Yoshifumi; Ushida, Akio; Ueno, Junji; Kasem, I.; Nishitani, Hiromu; Rekeczky, C.; Roska, T.

1997-01-01

In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully. (author)

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

Science.gov (United States)

Cho, Heeryon; Yoon, Sang Min

2018-04-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

Directory of Open Access Journals (Sweden)

Zeju Li

2017-01-01

Full Text Available This work proposed a novel automatic three-dimensional (3D magnetic resonance imaging (MRI segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN and fully connected conditional random field (CRF. Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

Evaluation of CNN architectures for gait recognition based on optical flow maps

OpenAIRE

Castro, F. M.; Marín-Jiménez, M.J.; Guil, N.; López-Tapia, S.; Pérez de la Blanca, N.

2017-01-01

This work targets people identification in video based on the way they walk (\\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (\\ie optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task. Universidad de Mál...

Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems

Energy Technology Data Exchange (ETDEWEB)

Raj, Sunny [Univ. of Central Florida, Orlando, FL (United States); Jha, Sumit Kumar [Univ. of Central Florida, Orlando, FL (United States); Pullum, Laura L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ramanathan, Arvind [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-05-01

Validating the correctness of human detection vision systems is crucial for safety applications such as pedestrian collision avoidance in autonomous vehicles. The enormous space of possible inputs to such an intelligent system makes it difficult to design test cases for such systems. In this report, we present our tool MAYA that uses an error model derived from a convolutional neural network (CNN) to explore the space of images similar to a given input image, and then tests the correctness of a given human or object detection system on such perturbed images. We demonstrate the capability of our tool on the pre-trained Histogram-of-Oriented-Gradients (HOG) human detection algorithm implemented in the popular OpenCV toolset and the Caffe object detection system pre-trained on the ImageNet benchmark. Our tool may serve as a testing resource for the designers of intelligent human and object detection systems.

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

Directory of Open Access Journals (Sweden)

Heeryon Cho

2018-04-01

Full Text Available Human Activity Recognition (HAR aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

Science.gov (United States)

Yoon, Sang Min

2018-01-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis.

Directory of Open Access Journals (Sweden)

Yanping Xue

Full Text Available Hip Osteoarthritis (OA is a common disease among the middle-aged and elderly people. Conventionally, hip OA is diagnosed by manually assessing X-ray images. This study took the hip joint as the object of observation and explored the diagnostic value of deep learning in hip osteoarthritis. A deep convolutional neural network (CNN was trained and tested on 420 hip X-ray images to automatically diagnose hip OA. This CNN model achieved a balance of high sensitivity of 95.0% and high specificity of 90.7%, as well as an accuracy of 92.8% compared to the chief physicians. The CNN model performance is comparable to an attending physician with 10 years of experience. The results of this study indicate that deep learning has promising potential in the field of intelligent medical image diagnosis practice.

CNN Newsroom Classroom Guides. September 1-30, 1994.

Science.gov (United States)

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of August provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) truce in Northern Ireland, school censorship, scientific method, burial…

CNN Newsroom Classroom Guides. June 1-30, 1994.

Science.gov (United States)

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of June provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) Congressman Dan Rostenkowski, D-Day, cars and Singapore, Rodney King civil…

GPU Boosted CNN Simulator Library for Graphical Flow-Based Programmability

Directory of Open Access Journals (Sweden)

Balázs Gergely Soós

2009-01-01

Full Text Available A graphical environment for CNN algorithm development is presented. The new generation of graphical cards with many general purpose processing units introduces the massively parallel computing into PC environment. Universal Machine on Flows- (UMF like notation, highlighting image flows and operations, is a useful tool to describe image processing algorithms. This documentation step can be turned into modeling using our framework backed with MATLAB Simulink and the power of a video card. This latter relatively cheap extension enables a convenient and fast analysis of CNN dynamics and complex algorithms. Comparison with other PC solutions is also presented. For single template execution, our approach yields run times 40x faster than that of the widely used Candy simulator. In the case of simpler algorithms, real-time execution is also possible.

PARTICLE SWARM OPTIMIZATION (PSO FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN

Directory of Open Access Journals (Sweden)

Arie Rachmad Syulistyo

2016-02-01

Full Text Available Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO in Convolutional Neural Networks (CNNs, which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification

Directory of Open Access Journals (Sweden)

Jongpil Lee

2018-01-01

Full Text Available Convolutional Neural Networks (CNN have been applied to diverse machine learning tasks for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw waveform-based approach has been explored to directly learn hierarchical characteristics of audio. However, the majority of previous studies have limited their model capacity by taking a frame-level structure similar to short-time Fourier transforms. We previously proposed a CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations. The architecture showed comparable performance to the spectrogram-based CNN model in music auto-tagging. In this paper, we extend the previous work in three ways. First, considering the sample-level model requires much longer training time, we progressively downsample the input signals and examine how it affects the performance. Second, we extend the model using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks. Finally, we visualize filters learned by the sample-level CNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency.

Determination of ionization energies of CnN (n=4-12): Vacuum-ultraviolet (VUV) photoionization experiments and theoretical calculations

International Nuclear Information System (INIS)

Kostko, Oleg; Zhou, Jia; Sun, Bian Jian; Lie, Jie Shiuan; Chang, Agnes H.H.; Kaiser, Ralf I.; Ahmed, Musahid

2010-01-01

Results from single photon vacuum ultraviolet photoionization of astrophysically relevant CnN clusters, n = 4 - 12, in the photon energy range of 8.0 eV to 12.8 eV are presented. The experimental photoionization efficiency curves, combined with electronic structure calculations, provide improved ionization energies of the CnN species. A search through numerous nitrogen-terminated CnN isomers for n=4-9 indicates that the linear isomer has the lowest energy, and therefore should be the most abundant isomer in the molecular beam. Comparison with calculated results also shed light on the energetics of the linear CnN clusters, particularly in the trends of the even-carbon and the odd-carbon series. These results can help guide the search of potential astronomical observations of these neutral molecules together with their cations in highly ionized regions or regions with a high UV/VUV photon flux (ranging from the visible to VUV with flux maxima in the Lyman-a region) in the interstellar medium.

SAR image classification based on CNN in real and simulation datasets

Science.gov (United States)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

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

2018-07-01

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.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

Science.gov (United States)

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

IReport for CNN Transmedia Storytelling On The Brazilian Protests in 2013

Directory of Open Access Journals (Sweden)

Geane Alzamora

2015-12-01

Full Text Available This study discusses the limits and potentials of the concept of transmedia storytelling to describe citizen coverage of the 2013 protests in Brazil in the collaborative section iReport for CNN on CNN.com. The section is characteristically intermedia because it connects to online social networks and doubles as a monthly television program with the same name. But to what extent could it also be characterized as transmedia? Systematic observation of the citizen coverage between June and July 2013 revealed a restructuring of certain editorial spaces on the site aimed at user-proposed perspectives as well as communicational activity across online social networks; both important aspects for its transmedia characterization. Furthermore, the visible hierarchical differentiation of journalistic reporting puts the transmediatic potential of the collaborative experiment into perspective by reducing the importance of expanding the narrative horizontally despite the study showing regular social scheduling for journalistic coverage as evidence of the dynamics of transmedia.

Development of JT-60 diagnostics system

International Nuclear Information System (INIS)

Suzuki, Yasuo

1988-01-01

The various kinds of plasma diagnostics have been developed and utilized in the JT-60 experiments. The features of JT-60 diagnostics system and the historical proceeding of the development are described in this paper. Taking account of the design consideration, JT-60 diagnostics system is sorted out into eight groups, which include six diagnostics systems, the data processing system and diagnostics supporting system. The all devices in the JT-60 diagnostics system were instrumented on schedule in the end of the fiscal year of 1985 and have contributed to JT-60 experiments. (author)

Framing the Tenth Anniversary of 9/11:  A Comparison of CNN and Phoenix TV commemorative websites

OpenAIRE

Zhuang, Yuxi

2013-01-01

It has been more than ten years since the 9/11 attacks in 2001, but the events related to the attacks are still a focus for the whole world. This study examined the news coverage of the 9/11 tenth anniversary from Phoenix TV and CNN, which are among the most influential news media in China and the U.S., respectively. A systematic content analysis was performed using latest news, opinion articles, photographs, and videos as classified by CNN and Phoenix TV on their commemorative 9/11 tenth ann...

MFTF-B plasma-diagnostic system

International Nuclear Information System (INIS)

Throop, A.L.; Goerz, D.A.; Thomas, S.R.

1981-01-01

This paper describes the current design status of the plasma diagnostic system for MFTF-B. In this paper we describe the system requirement changes which have occurred as a result of the funded rescoping of the original MFTF facility into MFTF-B. We outline the diagnostic instruments which are currently planned, and present an overview of the diagnostic system

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

Science.gov (United States)

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Measurements of Ay(θ) for 12C(n,n)12C from En=2.2 to 8.5 MeV

International Nuclear Information System (INIS)

Roper, C.D.; Tornow, W.; Braun, R.T.; Chen, Q.; Crowell, A.; Trotter, D. Gonzalez; Howell, C.R.; Salinas, F.; Setze, R.; Walter, R.L.; Chen Zemin; Tang Hongqing; Zhou Zuying

2005-01-01

The analyzing power A y (θ) for neutron elastic scattering from 12 C has been measured for 33 neutron energies between E n =2.2 and 8.5 MeV in the angular range from 25 deg. to 145 deg. in the laboratory system. The primary motivation for these measurements is the need for an accurate knowledge of A y (θ) for 12 C(n,n) 12 C elastic scattering to enable corrections to high-precision neutron-proton and neutron-deuteron A y (θ) data in the neutron-energy range below E n =30 MeV. In their own right, 12 C(n,n) 12 C A y (θ) data are of crucial importance for improving both the parametrization of n- 12 C scattering and our knowledge of the level scheme of 13 C. The present A y (θ) data are compared with published data and previous phase-shift-analysis results

CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS CNN-PROMOTER, NUEVO PROGRAMA PARA LA PREDICCIÓN DE PROMOTORES BASADO EN REDES NEURONALES CNN-PROMOTER, NOVO PROGRAMA PARA A PREDIÇÃO DE PROMOTORES BASEADO EM REDES NEURONAIS

Directory of Open Access Journals (Sweden)

Óscar Bedoya

2011-06-01

Full Text Available A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Markov's models, decision trees, and statistical methods. Although there are plenty of programs proposed, there is still a need to improve the sensitivity and specificity values. In this paper, a new program is proposed; it is based on the consensus strategy of using experts to make a better prediction. The consensus strategy is developed by using neural networks. During the training process, the sensitivity and specificity were 100 % and during the test process the model reaches a sensitivity of 74.5 % and a specificity of 82.7 %.En este artículo se presenta un programa nuevo para la predicción de promotores llamado CNN-Promoter, que toma como entrada secuencias de ADN y las clasifica como promotor o no promotor. Se han desarrollado diversos métodos para predecir las regiones promotoras en organismos eucariotas, muchos de los cuales se basan en modelos de Markov, árboles de decisión y métodos estadísticos. A pesar de la variedad de programas existentes para la predicción de promotores, se necesita aún mejorar los valores de sensibilidad y especificidad. Se propone un nuevo programa que se basa en la estrategia de mezcla de expertos usando redes neuronales. Los resultados obtenidos en las pruebas alcanzan valores de sensibilidad y especificidad de 100 % en el entrenamiento y de 74,5 % de sensibilidad y 82,7 % de especificidad en los conjuntos de validación y prueba.Neste artigo a presenta-se um novo programa para a predição de promotores chamado CNN-Promoter, que toma como entrada sequências de DNA e as classifica como promotor ou não promotor. Desenvolveramse diversos métodos para predizer as regiões promotoras em organismos eucariotas

Multi-stream CNN: Learning representations based on human-related regions for action recognition

NARCIS (Netherlands)

Tu, Zhigang; Xie, Wei; Qin, Qianqing; Poppe, R.W.; Veltkamp, R.C.; Li, Baoxin; Yuan, Junsong

2018-01-01

The most successful video-based human action recognition methods rely on feature representations extracted using Convolutional Neural Networks (CNNs). Inspired by the two-stream network (TS-Net), we propose a multi-stream Convolutional Neural Network (CNN) architecture to recognize human actions. We

ITER diagnostic system: Vacuum interface

Energy Technology Data Exchange (ETDEWEB)

Patel, K.M., E-mail: Kaushal.Patel@iter.org [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Udintsev, V.S.; Hughes, S.; Walker, C.I.; Andrew, P.; Barnsley, R.; Bertalot, L. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Drevon, J.M. [Bertin Technologies, BP 22, 13762 Aix-en Provence cedex 3 (France); Encheva, A. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France); Kashchuk, Y. [Institution “PROJECT CENTER ITER”, 1, Akademika Kurchatova pl., Moscow (Russian Federation); Maquet, Ph. [Bertin Technologies, BP 22, 13762 Aix-en Provence cedex 3 (France); Pearce, R.; Taylor, N.; Vayakis, G.; Walsh, M.J. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul-Lez-Durance (France)

2013-10-15

Diagnostics play an essential role for the successful operation of the ITER tokamak. They provide the means to observe control and to measure plasma during the operation of ITER tokamak. The components of the diagnostic system in the ITER tokamak will be installed in the vacuum vessel, in the cryostat, in the upper, equatorial and divertor ports, in the divertor cassettes and racks, as well as in various buildings. Diagnostic components that are placed in a high radiation environment are expected to operate for the life of ITER. There are approx. 45 diagnostic systems located on ITER. Some diagnostics incorporate direct or independently pumped extensions to maintain their necessary vacuum conditions. They require a base pressure less than 10{sup −7} Pa, irrespective of plasma operation, and a leak rate of less than 10{sup −10} Pa m{sup 3} s{sup −1}. In all the cases it is essential to maintain the ITER closed fuel cycle. These directly coupled diagnostic systems are an integral part of the ITER vacuum containment and are therefore subject to the same design requirements for tritium and active gas confinement, for all normal and accidental conditions. All the diagnostics, whether or not pumped, incorporate penetration of the vacuum boundary (i.e. window assembly, vacuum feedthrough etc.) and demountable joints. Monitored guard volumes are provided for all elements of the vacuum boundary that are judged to be vulnerable by virtue of their construction, material, load specification etc. Standard arrangements are made for their construction and for the monitoring, evacuating and leak testing of these volumes. Diagnostic systems are incorporated at more than 20 ports on ITER. This paper will describe typical and particular arrangements of pumped diagnostic and monitored guard volume. The status of the diagnostic vacuum systems, which are at the start of their detailed design, will be outlined and the specific features of the vacuum systems in ports and extensions

ITER diagnostic system: Vacuum interface

International Nuclear Information System (INIS)

Patel, K.M.; Udintsev, V.S.; Hughes, S.; Walker, C.I.; Andrew, P.; Barnsley, R.; Bertalot, L.; Drevon, J.M.; Encheva, A.; Kashchuk, Y.; Maquet, Ph.; Pearce, R.; Taylor, N.; Vayakis, G.; Walsh, M.J.

2013-01-01

Diagnostics play an essential role for the successful operation of the ITER tokamak. They provide the means to observe control and to measure plasma during the operation of ITER tokamak. The components of the diagnostic system in the ITER tokamak will be installed in the vacuum vessel, in the cryostat, in the upper, equatorial and divertor ports, in the divertor cassettes and racks, as well as in various buildings. Diagnostic components that are placed in a high radiation environment are expected to operate for the life of ITER. There are approx. 45 diagnostic systems located on ITER. Some diagnostics incorporate direct or independently pumped extensions to maintain their necessary vacuum conditions. They require a base pressure less than 10 −7 Pa, irrespective of plasma operation, and a leak rate of less than 10 −10 Pa m 3 s −1 . In all the cases it is essential to maintain the ITER closed fuel cycle. These directly coupled diagnostic systems are an integral part of the ITER vacuum containment and are therefore subject to the same design requirements for tritium and active gas confinement, for all normal and accidental conditions. All the diagnostics, whether or not pumped, incorporate penetration of the vacuum boundary (i.e. window assembly, vacuum feedthrough etc.) and demountable joints. Monitored guard volumes are provided for all elements of the vacuum boundary that are judged to be vulnerable by virtue of their construction, material, load specification etc. Standard arrangements are made for their construction and for the monitoring, evacuating and leak testing of these volumes. Diagnostic systems are incorporated at more than 20 ports on ITER. This paper will describe typical and particular arrangements of pumped diagnostic and monitored guard volume. The status of the diagnostic vacuum systems, which are at the start of their detailed design, will be outlined and the specific features of the vacuum systems in ports and extensions will be described

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

Science.gov (United States)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Science.gov (United States)

Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

2017-12-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

Scaling-Up the Functional Diagnostic Systems

International Nuclear Information System (INIS)

Mohamed, A.H.

2008-01-01

Functional diagnostic systems received a lot of attention in the last decade. They have proven their powerful for diagnosis the new faults of some complex systems. But, they still have some complexity in both the representation and reasoning about the large-scale systems. This paper introduces a new functional diagnostic system that can divide its small functions into main and auxiliary ones. This process enables the diagnostic system to scale -up the representation of the tested system and simplify the diagnostic mechanism tasks. Thus, it can improve both the representation and reasoning complexity. Also,it can decrease the required analysis, cost, and time. Proposed system can be applied for a wide area of the large-scale systems. It has been proven its acceptance to be applied practically for the Complex real-time systems

Nova target diagnostics control system

International Nuclear Information System (INIS)

Severyn, J.R.

1985-01-01

During the past year the Nova target diagnostics control system was finished and put in service. The diagnostics loft constructed to the north of the target room provides the environmental conditions required to collect reliable target diagnostic data. These improvements include equipment cooling and isolation of the power source with strict control of instrumentation grounds to eliminate data corruption due to electromagnetic pulses from the laser power-conditioning system or from target implosion effects

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

Science.gov (United States)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high

Applying Diagnostics to Enhance Cable System Reliability (Cable Diagnostic Focused Initiative, Phase II)

Energy Technology Data Exchange (ETDEWEB)

Hartlein, Rick [Georgia Tech Research Corporation (GTRC), Atlanta, GA (United States). National Electric Energy Testing, Research and Applications Center (NEETRAC); Hampton, Nigel [Georgia Tech Research Corporation (GTRC), Atlanta, GA (United States). National Electric Energy Testing, Research and Applications Center (NEETRAC); Perkel, Josh [Georgia Tech Research Corporation (GTRC), Atlanta, GA (United States). National Electric Energy Testing, Research and Applications Center (NEETRAC); Hernandez, JC [Univ. de Los Andes, Merida (Venezuela); Elledge, Stacy [Georgia Tech Research Corporation (GTRC), Atlanta, GA (United States). National Electric Energy Testing, Research and Applications Center (NEETRAC); del Valle, Yamille [Georgia Tech Research Corporation (GTRC), Atlanta, GA (United States). National Electric Energy Testing, Research and Applications Center (NEETRAC); Grimaldo, Jose [Georgia Inst. of Technology, Atlanta, GA (United States). School of Electrical and Computer Engineering; Deku, Kodzo [Georgia Inst. of Technology, Atlanta, GA (United States). George W. Woodruff School of Mechanical Engineering

2016-02-01

The Cable Diagnostic Focused Initiative (CDFI) played a significant and powerful role in clarifying the concerns and understanding the benefits of performing diagnostic tests on underground power cable systems. This project focused on the medium and high voltage cable systems used in utility transmission and distribution (T&D) systems. While many of the analysis techniques and interpretations are applicable to diagnostics and cable systems outside of T&D, areas such as generating stations (nuclear, coal, wind, etc.) and other industrial environments were not the focus. Many large utilities in North America now deploy diagnostics or have changed their diagnostic testing approach as a result of this project. Previous to the CDFI, different diagnostic technology providers individually promoted their approach as the “the best” or “the only” means of detecting cable system defects.

How Transferable are CNN-based Features for Age and Gender Classification?

OpenAIRE

Özbulak, Gökhan; Aytar, Yusuf; Ekenel, Hazım Kemal

2016-01-01

Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for a...

Cellular neural networks (CNN) simulation for the TN approximation of the time dependent neutron transport equation in slab geometry

International Nuclear Information System (INIS)

Hadad, Kamal; Pirouzmand, Ahmad; Ayoobian, Navid

2008-01-01

This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the time dependent one-speed neutron transport equation in slab geometry. We use a neutron angular flux in terms of the Chebyshev polynomials (T N ) of the first kind and then we attempt to implement the equations in an equivalent electrical circuit. We apply this equivalent circuit to analyze the T N moments equation in a uniform finite slab using Marshak type vacuum boundary condition. The validity of the CNN results is evaluated with numerical solution of the steady state T N moments equations by MATLAB. Steady state, as well as transient simulations, shows a very good comparison between the two methods. We used our CNN model to simulate space-time response of total flux and its moments for various c (where c is the mean number of secondary neutrons per collision). The complete algorithm could be implemented using very large-scale integrated circuit (VLSI) circuitry. The efficiency of the calculation method makes it useful for neutron transport calculations

Mining key elements for severe convection prediction based on CNN

Science.gov (United States)

Liu, Ming; Pan, Ning; Zhang, Changan; Sha, Hongzhou; Zhang, Bolei; Liu, Liang; Zhang, Meng

2017-04-01

Severe convective weather is a kind of weather disasters accompanied by heavy rainfall, gust wind, hail, etc. Along with recent developments on remote sensing and numerical modeling, there are high-volume and long-term observational and modeling data accumulated to capture massive severe convective events over particular areas and time periods. With those high-volume and high-variety weather data, most of the existing studies and methods carry out the dynamical laws, cause analysis, potential rule study, and prediction enhancement by utilizing the governing equations from fluid dynamics and thermodynamics. In this study, a key-element mining method is proposed for severe convection prediction based on convolution neural network (CNN). It aims to identify the key areas and key elements from huge amounts of historical weather data including conventional measurements, weather radar, satellite, so as numerical modeling and/or reanalysis data. Under this manner, the machine-learning based method could help the human forecasters on their decision-making on operational weather forecasts on severe convective weathers by extracting key information from the real-time and historical weather big data. In this paper, it first utilizes computer vision technology to complete the data preprocessing work of the meteorological variables. Then, it utilizes the information such as radar map and expert knowledge to annotate all images automatically. And finally, by using CNN model, it cloud analyze and evaluate each weather elements (e.g., particular variables, patterns, features, etc.), and identify key areas of those critical weather elements, then help forecasters quickly screen out the key elements from huge amounts of observation data by current weather conditions. Based on the rich weather measurement and model data (up to 10 years) over Fujian province in China, where the severe convective weathers are very active during the summer months, experimental tests are conducted with

Some aspects of diagnostic systems perspective

International Nuclear Information System (INIS)

Korosec, D.

1998-01-01

The integrity and safety of all nuclear power plant systems and components is guaranteed by the high requirements to quality assurance during all phases of design, fabrication, construction and operation. Many of the countries operating nuclear facilities, introduced advanced, sophisticated diagnostic systems for continuous monitoring safety important process parameters. The licensee should perform an assessment of the existing diagnostic systems, often supplied by the original design, their reliability and the need for the introduction of the additional monitoring/diagnostic systems. The operating experience should be taken into account and the assessment of the further needs. On this field has to be made on the results of PSA studies. In addition to the cost benefit analysis the evaluation of the new diagnostic systems in the light of nuclear safety should be also made. Experience, gained from the utilities, which have already installed this kind of the equipment should be very useful. Introducing new diagnostic systems will require often a safety assessment of the necessary modifications. Licensing process should be based on the existing nuclear legislation with certain additional requirements. (author)

A Real-Time Solution to the Image Segmentation Problem: CNN-Movels

OpenAIRE

Iannizzotto, Giancarlo; Lanzafame, Pietro; Rosa, Francesco La

2007-01-01

In this work we have described a re-formulation of a 2D still-image segmentation algorithm, implemented on a single-layer CNN, previously proposed (Iannizzotto, 2003). This algorithm is able to step-over limitation inherent to the class of active contours: sensitivity to insignificant false edges or "edge fragmentation". The approach features an iterative process of uniform shrinking and deformation of the active contour. Guided by statistical properties of edgeness of the image pixels, the c...

Robustness Design for CNN Templates with Performance of Extracting Closed Domain

International Nuclear Information System (INIS)

Li Weidong; Min Lequan

2006-01-01

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

R. Zhang

2016-06-01

Full Text Available Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs, called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

Thioaptamer Diagnostic System, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — AM Biotechnologies (AM) will develop a diagnostic system in response to SBIR Topic X10.01 Reusable Diagnostic Lab Technology that will simultaneously detect and...

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

Science.gov (United States)

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

OpenAIRE

Ren, Shaoqing; He, Kaiming; Girshick, Ross; Sun, Jian

2015-01-01

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultan...

Case-Based Fault Diagnostic System

International Nuclear Information System (INIS)

Mohamed, A.H.

2014-01-01

Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

Nonlinear Circuits and Neural Networks: Chip Implementation and Applications of the TeraOPS CNN Dynamic Array Supercomputer

National Research Council Canada - National Science Library

Chua, L

1998-01-01

.... Advances in research have been made in the following areas: (1) The design and implementation of the first-ever ARAM in the CNN Chip Set Architecture was successfully competed, and the samples were successfully tested; (2...

Psychometric perspectives on diagnostic systems

NARCIS (Netherlands)

Borsboom, D.

2008-01-01

The author identifies four conceptualizations of the relation between symptoms and disorders as utilized in diagnostic systems such as the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994): A constructivist perspective, which holds

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

Science.gov (United States)

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Expert system for fast reactor diagnostic

International Nuclear Information System (INIS)

Parcy, J.P.

1982-09-01

A general description of expert systems is given. The operation of a fast reactor is reviewed. The expert system to the diagnosis of breakdowns limited to the reactor core. The structure of the system is described: specification of the diagnostics; structure of the data bank and evaluation of the rules; specification of the prediagnostics and evaluation; explanation of the diagnostics; time evolution of the system; comparison with other expert systems. Applications to some cases of faults are finally presented [fr

5W1H Information Extraction with CNN-Bidirectional LSTM

Science.gov (United States)

Nurdin, A.; Maulidevi, N. U.

2018-03-01

In this work, information about who, did what, when, where, why, and how on Indonesian news articles were extracted by combining Convolutional Neural Network and Bidirectional Long Short-Term Memory. Convolutional Neural Network can learn semantically meaningful representations of sentences. Bidirectional LSTM can analyze the relations among words in the sequence. We also use word embedding word2vec for word representation. By combining these algorithms, we obtained F-measure 0.808. Our experiments show that CNN-BLSTM outperforms other shallow methods, namely IBk, C4.5, and Naïve Bayes with the F-measure 0.655, 0.645, and 0.595, respectively.

A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining

Directory of Open Access Journals (Sweden)

Yohei Koga

2018-01-01

Full Text Available Recently, deep learning techniques have had a practical role in vehicle detection. While much effort has been spent on applying deep learning to vehicle detection, the effective use of training data has not been thoroughly studied, although it has great potential for improving training results, especially in cases where the training data are sparse. In this paper, we proposed using hard example mining (HEM in the training process of a convolutional neural network (CNN for vehicle detection in aerial images. We applied HEM to stochastic gradient descent (SGD to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses. We picked 100 out of both 500 and 1000 examples for training in one iteration, and we tested different ratios of positive to negative examples in the training data to evaluate how the balance of positive and negative examples would affect the performance. In any case, our method always outperformed the plain SGD. The experimental results for images from New York showed improved performance over a CNN trained in plain SGD where the F1 score of our method was 0.02 higher.

Lane marking detection based on waveform analysis and CNN

Science.gov (United States)

Ye, Yang Yang; Chen, Hou Jin; Hao, Xiao Li

2017-06-01

Lane markings detection is a very important part of the ADAS to avoid traffic accidents. In order to obtain accurate lane markings, in this work, a novel and efficient algorithm is proposed, which analyses the waveform generated from the road image after inverse perspective mapping (IPM). The algorithm includes two main stages: the first stage uses an image preprocessing including a CNN to reduce the background and enhance the lane markings. The second stage obtains the waveform of the road image and analyzes the waveform to get lanes. The contribution of this work is that we introduce local and global features of the waveform to detect the lane markings. The results indicate the proposed method is robust in detecting and fitting the lane markings.

Intelligent neural network diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

Synthesis of technetium-99m labeled clinafloxacin (99mTc-CNN) complex and biological evaluation as a potential Staphylococcus aureus infection imaging agent

International Nuclear Information System (INIS)

Syed Qaiser Shah; Muhammad Rafiullah Khan

2011-01-01

In the present study synthesis of the 99m Tc-CNN complex and its efficacy as a prospective Staphylococcus aureus (S. aureus) infection imaging agent was assessed. The 99m Tc-CNN complex was characterized in terms of stability in saline, serum, in vitro binding with S. aureus and in vivo percent absorption in male Wister rats (MWR) infected with live and heat killed S. aureus. Radiochemically the 99m Tc-CNN complex showed stable behavior in saline and serum at different intervals. At 30 min after reconstitution the complex showed maximum radiochemical purity (RCP) yield of 97.55 ± 0.22%. The RCP yield decreased to 90.50 ± 0.18% within 240 min. In serum, 18.15% unwanted side product was appeared within 16 h of the incubation. In vitro saturated binding with S. aureus was observed at different intervals with a 62.00% maximum at 90 min. Normal percent in vivo uptake was observed in MWR artificially infected with live S. aureus with a five times higher in the infected muscle as compared to the inflamed and normal muscles. No difference in the percent uptake of the complex in MWR infected with heat killed S. aureus in the infected, inflamed and normal muscles were observed. Based on the promising in vitro and in vivo radiochemical and biological characteristics, we recommend the 99m Tc-CNN complex for in vivo localization of the S. aureus infectious foci. (author)

Nuclear power plant diagnostic system

International Nuclear Information System (INIS)

Prokop, K.; Volavy, J.

1982-01-01

Basic information is presented on diagnostic systems used at nuclear power plants with PWR reactors. They include systems used at the Novovoronezh nuclear power plant in the USSR, at the Nord power plant in the GDR, the system developed at the Hungarian VEIKI institute, the system used at the V-1 nuclear power plant at Jaslovske Bohunice in Czechoslovakia and systems of the Rockwell International company used in US nuclear power plants. These diagnostic systems are basically founded on monitoring vibrations and noise, loose parts, pressure pulsations, neutron noise, coolant leaks and acoustic emissions. The Rockwell International system represents a complex unit whose advantage is the on-line evaluation of signals which gives certain instructions for the given situation directly to the operator. The other described systems process signals using similar methods. Digitized signals only serve off-line computer analyses. (Z.M.)

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Science.gov (United States)

Kalia, Subodh; Ganguly, Sangram; Li, Shuang; Nemani, Ramakrishna R.

2017-01-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

IMPLEMENTATION OF TURNOUTS TECHNICAL DIAGNOSTICS SYSTEMS

Directory of Open Access Journals (Sweden)

S. YU. Buryak

2015-06-01

Full Text Available Purpose. In the paper it is necessary to: 1 find out the causes of turnouts faults to determine diagnostic features failures; 2 consider the requirements structure, purpose components of turnouts, work and technology of their maintenance to determine the construction of the economic activities related to system to the turnout’s maintenance; 3 substantiate the possibility, necessity and prospects of automated diagnostics turnout’s implementation; 4 elaborate a prototype of an automated hardware and software system for the turnouts control parameters and perform diagnostics on them. Methodology. In the paper possible turnouts faults were presented and manifestations and influence on its work were shown. According to the current technology works the process analyze of turnouts’ maintenance was conducted, were defined the basic performed operations during the examination of appearance, parameters and check the repair or replacement of parts and assemblies. Based on the analysis of reasons of turnouts malfunctioning and their fixes were systematized types of damages and ways to deal with them, an information scheme of troubleshooting were created, opportunities and limits of automating the process of diagnostics were identified and compared with the existing method of turnouts maintenance. A diagnostics system block diagram was created, an algorithm of its work was developed and established main basic principles of operation. Software and hardware to determine the turnout’s state considering diagnostic performance of points in use were applied. Findings. During the experiment was created a method of automated turnout’s diagnostics with AC electric drives, managed centrally. The results of automated hardware and software system make it possible to control turnout’s parameters and perform diagnostics on them. Originality. Authors created the method of turnout’s state determination by current curve and its spectral composition in the

ATA diagnostic data handling system: an overview

International Nuclear Information System (INIS)

Chambers, F.W.; Kallman, J.; McDonald, J.; Slominski, M.

1984-01-01

The functions to be performed by the ATA diagnostic data handling system are discussed. The capabilities of the present data acquisition system (System 0) are presented. The goals for the next generation acquisition system (System 1), currently under design, are discussed. Facilities on the Octopus system for data handling are reviewed. Finally, we discuss what has been learned about diagnostics and computer based data handling during the past year

A Self-Diagnostic System for the M6 Accelerometer

Science.gov (United States)

Flanagan, Patrick M.; Lekki, John

2001-01-01

The design of a Self-Diagnostic (SD) accelerometer system for the Space Shuttle Main Engine is presented. This retrofit system connects diagnostic electronic hardware and software to the current M6 accelerometer system. This paper discusses the general operation of the M6 accelerometer SD system and procedures for developing and evaluating the SD system. Signal processing techniques using M6 accelerometer diagnostic data are explained. Test results include diagnostic data responding to changing ambient temperature, mounting torque and base mounting impedance.

Reliability and diagnostic of modular systems

Directory of Open Access Journals (Sweden)

J. Kohlas

2014-01-01

Full Text Available Reliability and diagnostic are in general two problems discussed separately. Yet the two problems are in fact closely related to each other. Here, this relation is considered in the simple case of modular systems. We show, how the computation of reliability and diagnostic can efficiently be done within the same Bayesian network induced by the modularity of the structure function of the system.

Radiation effects in IFMIF Li target diagnostic systems

International Nuclear Information System (INIS)

Molla, J.; Vila, R.; Shikama, T.; Horiike, H.; Simakov, S.; Ciotti, M.; Ibarra, A.

2009-01-01

Diagnostics for the lithium target will be crucial for the operation of IFMIF. Several parameters as the lithium temperature, target thickness or wave pattern must be monitored during operation. Radiation effects may produce malfunctioning in any of these diagnostics due to the exposure to high radiation fields. The main diagnostic systems proposed for the operation of IFMIF are reviewed in this paper from the point of view of radiation damage. The main tools for the assessment of the performance of these diagnostics are the neutronics calculations by using specialised codes and the information accumulated during the last decades on the radiation effects in functional materials, components and diagnostics for ITER. This analysis allows to conclude that the design of some of the diagnostic systems must be revised to assure the high availability required for the target system.

MFTF plasma diagnostics data acquisition system

International Nuclear Information System (INIS)

Davis, G.E.; Coffield, F.E.

1979-01-01

The initial goal of the Data Acquisition System (DAS) is to control 11 instruments chosen as the startup diagnostic set and to collect, process, and display the data that these instruments produce. These instruments are described in a paper by Stan Thomas, et. al. entitled ''MFTF Plasma Diagnostics System.'' The DAS must be modular and flexible enough to allow upgrades in the quantity of data taken by an instrument, and also to allow new instruments to be added to the system. This is particularly necessary to support a research project where needs and requirements may change rapidly as a result of experimental findings. Typically, the startup configuration of the diagnostic instruments will contain only a fraction of the planned detectors, and produce approximately one half the data that the expanded version is designed to generate. Expansion of the system will occur in fiscal year 1982

Residual heat removal system diagnostic advisor

International Nuclear Information System (INIS)

Tripp, L.

1991-01-01

This paper reports on the Residual Heat Removal System (RHRS) Diagnostic Advisor which is an expert system designed to alert the operators to abnormal conditions that exits in the RHRS and offer advice about the cause of the abnormal conditions. The Advisor uses a combination of rule-based and model-based diagnostic techniques to perform its functions. This diagnostic approach leads to a deeper understanding of the RHRS by the Advisor and consequently makes it more robust to unexpected conditions. The main window of the interactive graphic display is a schematic diagram of the RHRS piping system. When a conclusion about a failed component can be reached, the operator can bring up windows that describe the failure mode of the component and a brief explanation about how the Advisor arrived at its conclusion

NPP Mochovce units 1 and 2 diagnostic systems

International Nuclear Information System (INIS)

Heidenreich, S.

1997-01-01

In this paper the diagnostic systems (leak detection monitoring, vibration monitoring, lose parts monitoring, fatigue monitoring) of NPP Mochovce units 1 and 2 are presented. All of the designed diagnostic systems are personal computer based systems

Mechatronics in design of monitoring and diagnostic systems

Energy Technology Data Exchange (ETDEWEB)

Uhl, T.; Barszcz, T. [Univ. of Mining and Metallurgy, Krakow (Poland); Hanc, A. [Energocontrol Ltd., Krakow (Poland)

2003-07-01

Nowadays development of computer engineering in area of hardware and software gives new possibilities of monitoring and diagnostics system design. The paper presents analysis of new possible solutions for design of monitoring and diagnostic systems including; smart sensor design, modular software design and communication modules. New concept of monitoring system based on home page server solution (nano-server) is presented. Smart sensor design concept with embedded hardware for diagnostic application is shown. New software concept for monitoring and diagnostics automation and examples of applications of new design for condition monitoring based on proposed solution are carefully discussed. (orig.)

Imaging systems for medical diagnostics

International Nuclear Information System (INIS)

Krestel, E.

1990-01-01

This book provides physicians and clinical physicists with detailed information on today's imaging modalities and assists them in selecting the optimal system for each clinical application. Physicists, engineers and computer specialists engaged in research and development and sales departments will also find this book to be of considerable use. It may also be employed at universities, training centers and in technical seminars. The physiological and physical fundamentals are explained in part 1. The technical solutions contained in part 2 illustrate the numerous possibilities available in X-ray diagnostics, computed tomography, nuclear medical diagnostics, magnetic resonance imaging, sonography and biomagnetic diagnostics. (orig.)

An easy-to-use diagnostic system development shell

Science.gov (United States)

Tsai, L. C.; Ross, J. B.; Han, C. Y.; Wee, W. G.

1987-01-01

The Diagnostic System Development Shell (DSDS), an expert system development shell for diagnostic systems, is described. The major objective of building the DSDS is to create a very easy to use and friendly environment for knowledge engineers and end-users. The DSDS is written in OPS5 and CommonLisp. It runs on a VAX/VMS system. A set of domain independent, generalized rules is built in the DSDS, so the users need not be concerned about building the rules. The facts are explicitly represented in a unified format. A powerful check facility which helps the user to check the errors in the created knowledge bases is provided. A judgement facility and other useful facilities are also available. A diagnostic system based on the DSDS system is question driven and can call or be called by other knowledge based systems written in OPS5 and CommonLisp. A prototype diagnostic system for diagnosing a Philips constant potential X-ray system has been built using the DSDS.

A Diagnostic Ultrasound Imaging System

International Nuclear Information System (INIS)

Lee, Seong Woo

1999-01-01

The ability to see the internal organs of the human body in a noninvasive way is a powerful diagnostic tool of modern medicine. Among these imaging modalities such as X-ray, MRI, and ultrasound. MRI and ultrasound are presenting much less risk of undesirable damage of both patient and examiner. In fact, no deleterious effects have been reported as a result of clinical examination by using MRI and ultrasound diagnostic equipment. As a result, their market volume has been rapidly increased. MRI has a good resolution. but there are a few disadvantages such as high price. non-real-time imaging capability. and expensive diagnostic cost. On the other hand, the ultrasound imaging system has inherently poor resolution as compared with X-ray and MRI. In spite of its poor resolution, the ultrasound diagnostic equipment is lower in price and has an ability of real-time imaging as compared with the others. As a result, the ultrasound imaging system has become general and essential modality for imaging the internal organs of human body. In this review various researches and developments to enhance the resolution of the ultrasound images are explained and future trends of the ultrasound imaging technology are described

MTX [Microwave Tokamak Experiment] plasma diagnostic system

International Nuclear Information System (INIS)

Rice, B.W.; Hooper, E.B.; Brooksby, C.A.

1987-01-01

In this paper, a general overview of the MTX plasma diagnostics system is given. This includes a description of the MTX machine configuration and the overall facility layout. The data acquisition system and techniques for diagnostic signal transmission are also discussed. In addition, the diagnostic instruments planned for both an initial ohmic-heating set and a second FEL-heating set are described. The expected range of plasma parameters along with the planned plasma measurements will be reviewed. 7 refs., 5 figs

Study for the design method of multi-agent diagnostic system to improve diagnostic performance for similar abnormality

International Nuclear Information System (INIS)

Minowa, Hirotsugu; Gofuku, Akio

2014-01-01

Accidents on industrial plants cause large loss on human, economic, social credibility. In recent, studies of diagnostic methods using techniques of machine learning such as support vector machine is expected to detect the occurrence of abnormality in a plant early and correctly. There were reported that these diagnostic machines has high accuracy to diagnose the operating state of industrial plant under mono abnormality occurrence. But the each diagnostic machine on the multi-agent diagnostic system may misdiagnose similar abnormalities as a same abnormality if abnormalities to diagnose increases. That causes that a single diagnostic machine may show higher diagnostic performance than one of multi-agent diagnostic system because decision-making considering with misdiagnosis is difficult. Therefore, we study the design method for multi-agent diagnostic system to diagnose similar abnormality correctly. This method aimed to realize automatic generation of diagnostic system where the generation process and location of diagnostic machines are optimized to diagnose correctly the similar abnormalities which are evaluated from the similarity of process signals by statistical method. This paper explains our design method and reports the result evaluated our method applied to the process data of the fast-breeder reactor Monju

Supervisory Control and Diagnostics System Distributed Operating System

International Nuclear Information System (INIS)

McGoldrick, P.R.

1979-01-01

This paper contains a description of the Supervisory Control and Diagnostics System (SCDS) Distributed Operating System. The SCDS consists of nine 32-bit minicomputers with shared memory. The system's main purpose is to control a large Mirror Fusion Test Facility

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

Directory of Open Access Journals (Sweden)

Paolo Napoletano

2018-01-01

Full Text Available Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

Diagnostic system for primary circuits of pressurized-water reactors

International Nuclear Information System (INIS)

Liska, J.; Majer, J.

1983-01-01

The diagnostic system monitors the reactor, the main circulating pipe, the main circulating pump, the main shut-off valve, the steam generator and the pressurizer. Diagnostic signals are obtained from the sensors designed for operation measurements and from sensors for special diagnostic purposes. The following operations are carried out: detection of dangerous dynamic stress of components, detection of damage to functional surfaces of components, detection of occurrence and propagation of defects in component materials, detection of loose particles and foreign bodies, detection of coolant leakage, detection of coolant boiling in the core and detection of impermissible non-homogeneities of fields of physical quantities in the core. The diagnostic system comprises: monitoring, classification of properly investigated effects, periodical tracing and long-term tracing. The operational diagnostics system developed by the SKODA Concern consists of a vibration monitoring system, a spectral analysis system and a central evaluation system. (M.D.)

Resilient actions in the diagnostic process and system performance.

Science.gov (United States)

Smith, Michael W; Davis Giardina, Traber; Murphy, Daniel R; Laxmisan, Archana; Singh, Hardeep

2013-12-01

Systemic issues can adversely affect the diagnostic process. Many system-related barriers can be masked by 'resilient' actions of frontline providers (ie, actions supporting the safe delivery of care in the presence of pressures that the system cannot readily adapt to). We explored system barriers and resilient actions of primary care providers (PCPs) in the diagnostic evaluation of cancer. We conducted a secondary data analysis of interviews of PCPs involved in diagnostic evaluation of 29 lung and colorectal cancer cases. Cases covered a range of diagnostic timeliness and were analysed to identify barriers for rapid diagnostic evaluation, and PCPs' actions involving elements of resilience addressing those barriers. We rated these actions according to whether they were usual or extraordinary for typical PCP work. Resilient actions and associated barriers were found in 59% of the cases, in all ranges of timeliness, with 40% involving actions rated as beyond typical. Most of the barriers were related to access to specialty services and coordination with patients. Many of the resilient actions involved using additional communication channels to solicit cooperation from other participants in the diagnostic process. Diagnostic evaluation of cancer involves several resilient actions by PCPs targeted at system deficiencies. PCPs' actions can sometimes mitigate system barriers to diagnosis, and thereby impact the sensitivity of 'downstream' measures (eg, delays) in detecting barriers. While resilient actions might enable providers to mitigate system deficiencies in the short run, they can be resource intensive and potentially unsustainable. They complement, rather than substitute for, structural remedies to improve system performance. Measures to detect and fix system performance issues targeted by these resilient actions could facilitate diagnostic safety.

Diagnostic Neural Network Systems for the Electronic Circuits

International Nuclear Information System (INIS)

Mohamed, A.H.

2014-01-01

Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

Expert system applications in support of system diagnostics and prognostics at EBR-II

International Nuclear Information System (INIS)

Lehto, W.K.; Gross, K.C.

1989-01-01

Expert systems have been developed to aid in the monitoring and diagnostics of the Experimental Breeder Reactor-II (EBR-II) at the Idaho National Engineering Laboratory (INEL) in Idaho Falls, Idaho. Systems have been developed for failed fuel surveillance and diagnostics and reactor coolant pump monitoring and diagnostics. A third project is being done jointly by ANL-W and EG ampersand G Idaho to develop a transient analysis system to enhance overall plant diagnostic and prognostic capability. The failed fuel surveillance and diagnosis system monitors, processes, and interprets information from nine key plant sensors. It displays to the reactor operator diagnostic information needed to make proper decisions regarding technical specification conformance during reactor operation with failed fuel. 8 refs., 9 figs., 2 tabs

Knowledge acquisition for nuclear power plant unit diagnostic system

International Nuclear Information System (INIS)

Li Xiaodong; Xi Shuren

2003-01-01

The process of acquiring knowledge and building a knowledge base is critical to realize fault diagnostic system at unit level in a nuclear power plant. It directly determines whether the diagnostic system can be applied eventually in a commercial plant. A means to acquire knowledge and its procedures was presented in this paper for fault diagnostic system in a nuclear power plant. The work can be carried out step by step and it is feasible in a commercial nuclear power plant. The knowledge base of the fault diagnostic system for a nuclear power plant can be built after the staff finish the tasks according to the framework presented in this paper

Fusing Panchromatic and SWIR Bands Based on Cnn - a Preliminary Study Over WORLDVIEW-3 Datasets

Science.gov (United States)

Guo, M.; Ma, H.; Bao, Y.; Wang, L.

2018-04-01

The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpening method for the fusion of PAN and SWIR (short-wave infrared) bands, whose spectral coverages are not overlapping. This problem is addressed with a convolutional neural network (CNN), which is trained by WorldView-3 dataset. CNN can learn the complex relationship among bands, and thus alleviate spectral distortion. Consequently, in our network, we use the simple three-layer basic architecture with 16 × 16 kernels to conduct the experiment. Every layer use different receptive field. The first two layers compute 512 feature maps by using the 16 × 16 and 1 × 1 receptive field respectively and the third layer with a 8 × 8 receptive field. The fusion results are optimized by continuous training. As for assessment, four evaluation indexes including Entropy, CC, SAM and UIQI are selected built on subjective visual effect and quantitative evaluation. The preliminary experimental results demonstrate that the fusion algorithms can effectively enhance the spatial information. Unfortunately, the fusion image has spectral distortion, it cannot maintain the spectral information of the SWIR image.

Laboratory Information Systems in Molecular Diagnostics: Why Molecular Diagnostics Data are Different.

Science.gov (United States)

Lee, Roy E; Henricks, Walter H; Sirintrapun, Sahussapont J

2016-03-01

Molecular diagnostic testing presents new challenges to information management that are yet to be sufficiently addressed by currently available information systems for the molecular laboratory. These challenges relate to unique aspects of molecular genetic testing: molecular test ordering, informed consent issues, diverse specimen types that encompass the full breadth of specimens handled by traditional anatomic and clinical pathology information systems, data structures and data elements specific to molecular testing, varied testing workflows and protocols, diverse instrument outputs, unique needs and requirements of molecular test reporting, and nuances related to the dissemination of molecular pathology test reports. By satisfactorily addressing these needs in molecular test data management, a laboratory information system designed for the unique needs of molecular diagnostics presents a compelling reason to migrate away from the current paper and spreadsheet information management that many molecular laboratories currently use. This paper reviews the issues and challenges of information management in the molecular diagnostics laboratory.

BWR recirculation pump diagnostic expert system

International Nuclear Information System (INIS)

Chiang, S.C.; Morimoto, C.N.; Torres, M.R.

2004-01-01

At General Electric (GE), an on-line expert system to support maintenance decisions for BWR recirculation pumps for nuclear power plants has been developed. This diagnostic expert system is an interactive on-line system that furnishes diagnostic information concerning BWR recirculation pump operational problems. It effectively provides the recirculation pump diagnostic expertise in the plant control room continuously 24 hours a day. The expert system is interfaced to an on-line monitoring system, which uses existing plant sensors to acquire non-safety related data in real time. The expert system correlates and evaluates process data and vibration data by applying expert rules to determine the condition of a BWR recirculation pump system by applying knowledge based rules. Any diagnosis will be automatically displayed, indicating which pump may have a problem, the category of the problem, and the degree of concern expressed by the validity index and color hierarchy. The rules incorporate the expert knowledge from various technical sources such as plant experience, engineering principles, and published reports. These rules are installed in IF-THEN formats and the resulting truth values are also expressed in fuzzy terms and a certainty factor called a validity index. This GE Recirculation Pump Expert System uses industry-standard software, hardware, and network access to provide flexible interfaces with other possible data acquisition systems. Gensym G2 Real-Time Expert System is used for the expert shell and provides the graphical user interface, knowledge base, and inference engine capabilities. (author)

TFTR diagnostic control and data acquisition system

International Nuclear Information System (INIS)

Sauthoff, N.R.; Daniels, R.E.; PPL Computer Division

1985-01-01

General computerized control and data-handling support for TFTR diagnostics is presented within the context of the Central Instrumentation, Control and Data Acquisition (CICADA) System. Procedures, hardware, the interactive man--machine interface, event-driven task scheduling, system-wide arming and data acquisition, and a hierarchical data base of raw data and results are described. Similarities in data structures involved in control, monitoring, and data acquisition afford a simplification of the system functions, based on ''groups'' of devices. Emphases and optimizations appropriate for fusion diagnostic system designs are provided. An off-line data reduction computer system is under development

A recommender system for medical imaging diagnostic.

Science.gov (United States)

Monteiro, Eriksson; Valente, Frederico; Costa, Carlos; Oliveira, José Luís

2015-01-01

The large volume of data captured daily in healthcare institutions is opening new and great perspectives about the best ways to use it towards improving clinical practice. In this paper we present a context-based recommender system to support medical imaging diagnostic. The system relies on data mining and context-based retrieval techniques to automatically lookup for relevant information that may help physicians in the diagnostic decision.

Target diagnostic system for the National Ignition Facility (NIF)

International Nuclear Information System (INIS)

Leeper, R.J.; Chandler, G.A.; Cooper, G.W.; Derzon, M.S.

1996-01-01

A review of recent progress on the design of a diagnostic system proposed for ignition target experiments on the National Ignition Facility (NIF) will be presented. This diagnostic package contains an extensive suite of optical, x-ray, gamma-ray, and neutron diagnostics that enable measurements of the performance of both direct and indirect driven NIF targets. The philosophy used in designing all of the diagnostics in the set has emphasized redundant and independent measurement of fundamental physical quantities relevant to the operation of the NIF target. A unique feature of these diagnostics is that they are being designed to be capable of operating, in the high radiation, EMP, and debris backgrounds expected on the NIF facility. The diagnostic system proposed can be categorized into three broad areas: laser characterization, hohlraum characterization, and capsule performance diagnostics. The operating principles of a representative instrument from each class of diagnostic employed in this package will be summarized and illustrated with data obtained in recent prototype diagnostic tests

Thioaptamer Diagnostic System, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — AM Biotechnologies (AM) in partnership with Sandia National Laboratories will develop a Thioaptamer Diagnostic System (TDS) in response to Topic X10.01 Reusable...

Beam profile diagnostics system for SDUV-FEL

International Nuclear Information System (INIS)

Xu Yichao; Han Lifeng; Chen Yongzhong

2010-01-01

A new beam profile diagnostics system for Shanghai Deep Ultraviolet Free Electron Laser (SDUV-FEL) has been developed based on industrial Ethernet, with good versatility and scalability. The system includes three major subsystems for image acquisition,pneumatic control and stepper motor control, respectively. Virtual instrument technology is adopted to drive the devices, and to develop the measurement software. In this paper,we describe the system structure, and its hardware and software design. The results of system commissioning are given as well. As an important diagnostic tool and data acquisition method, the system has been successfully applied to the measurement and control of the SDUV-FEL.(authors)

Development trends for diagnostic systems in nuclear power plants

International Nuclear Information System (INIS)

Kunze, U.; Pohl, U.

1998-01-01

Monitoring systems used in nuclear power plants have made remarkable progress over the past four or five years. Development has followed the trends and changes in philosophy for the purpose of monitoring systems in nuclear power plants: They are no longer expected to fulfill only safety tasks, the plant personnel require information on which to base condition-oriented maintenance. A new generation of monitoring and diagnostic systems has been developed by Siemens recently. This new generation, called Series '95, is PC-based. An overview is given for the KUeS '95 loose parts diagnostic system, the SUeS '95 vibration monitoring system, the FLUeS leak detection system and the SIPLUG valve diagnostics system. The objectives behind the development of these new systems are both safety-related and economic. The new systems improve the reliability and quality of monitoring techniques and incorporate better detection and diagnostic capabilities. Progress has also been made in automation of the systems so as to reduce routine work, give higher sensitivity for the monitoring task and reduce the scope of maintenance. (author)

Proton storage ring (PSR) diagnostics and control system

International Nuclear Information System (INIS)

Clout, P.

1983-01-01

When any new accelerator or storage ring is built that advances the state of the art, the diagnostic system becomes extremely important in tuning the facility to full specification. This paper will discuss the various diagnostic devices planned or under construction for the PSR and their connection into the control system

A CLOUD BOUNDARY DETECTION SCHEME COMBINED WITH ASLIC AND CNN USING ZY-3, GF-1/2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

Z. Guo

2018-04-01

Full Text Available Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

Opinion mining on book review using CNN-L2-SVM algorithm

Science.gov (United States)

Rozi, M. F.; Mukhlash, I.; Soetrisno; Kimura, M.

2018-03-01

Review of a product can represent quality of a product itself. An extraction to that review can be used to know sentiment of that opinion. Process to extract useful information of user review is called Opinion Mining. Review extraction model that is enhancing nowadays is Deep Learning model. This Model has been used by many researchers to obtain excellent performance on Natural Language Processing. In this research, one of deep learning model, Convolutional Neural Network (CNN) is used for feature extraction and L2 Support Vector Machine (SVM) as classifier. These methods are implemented to know the sentiment of book review data. The result of this method shows state-of-the art performance in 83.23% for training phase and 64.6% for testing phase.

Benchmarking Diagnostic Algorithms on an Electrical Power System Testbed

Science.gov (United States)

Kurtoglu, Tolga; Narasimhan, Sriram; Poll, Scott; Garcia, David; Wright, Stephanie

2009-01-01

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model-based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies. In this paper, we present our efforts to benchmark a set of DAs on a common platform using a framework that was developed to evaluate and compare various performance metrics for diagnostic technologies. The diagnosed system is an electrical power system, namely the Advanced Diagnostics and Prognostics Testbed (ADAPT) developed and located at the NASA Ames Research Center. The paper presents the fundamentals of the benchmarking framework, the ADAPT system, description of faults and data sets, the metrics used for evaluation, and an in-depth analysis of benchmarking results obtained from testing ten diagnostic algorithms on the ADAPT electrical power system testbed.

Systematic Benchmarking of Diagnostic Technologies for an Electrical Power System

Science.gov (United States)

Kurtoglu, Tolga; Jensen, David; Poll, Scott

2009-01-01

Automated health management is a critical functionality for complex aerospace systems. A wide variety of diagnostic algorithms have been developed to address this technical challenge. Unfortunately, the lack of support to perform large-scale V&V (verification and validation) of diagnostic technologies continues to create barriers to effective development and deployment of such algorithms for aerospace vehicles. In this paper, we describe a formal framework developed for benchmarking of diagnostic technologies. The diagnosed system is the Advanced Diagnostics and Prognostics Testbed (ADAPT), a real-world electrical power system (EPS), developed and maintained at the NASA Ames Research Center. The benchmarking approach provides a systematic, empirical basis to the testing of diagnostic software and is used to provide performance assessment for different diagnostic algorithms.

Evaluation of CROES Nephrolithometry Nomogram as a Preoperative Predictive System for Percutaneous Nephrolithotomy Outcomes.

Science.gov (United States)

Kumar, Sumit; Sreenivas, Jayaram; Karthikeyan, Vilvapathy Senguttuvan; Mallya, Ashwin; Keshavamurthy, Ramaiah

2016-10-01

Scoring systems have been devised to predict outcomes of percutaneous nephrolithotomy (PCNL). CROES nephrolithometry nomogram (CNN) is the latest tool devised to predict stone-free rate (SFR). We aim to compare predictive accuracy of CNN against Guy stone score (GSS) for SFR and postoperative outcomes. Between January 2013 and December 2015, 313 patients undergoing PCNL were analyzed for predictive accuracy of GSS, CNN, and stone burden (SB) for SFR, complications, operation time (OT), and length of hospitalization (LOH). We further stratified patients into risk groups based on CNN and GSS. Mean ± standard deviation (SD) SB was 298.8 ± 235.75 mm 2 . SB, GSS, and CNN (area under curve [AUC]: 0.662, 0.660, 0.673) were found to be predictors of SFR. However, predictability for complications was not as good (AUC: SB 0.583, GSS 0.554, CNN 0.580). Single implicated calix (Adj. OR 3.644; p = 0.027), absence of staghorn calculus (Adj. OR 3.091; p = 0.044), single stone (Adj. OR 3.855; p = 0.002), and single puncture (Adj. OR 2.309; p = 0.048) significantly predicted SFR on multivariate analysis. Charlson comorbidity index (CCI; p = 0.020) and staghorn calculus (p = 0.002) were independent predictors for complications on linear regression. SB and GSS independently predicted OT on multivariate analysis. SB and complications significantly predicted LOH, while GSS and CNN did not predict LOH. CNN offered better risk stratification for residual stones than GSS. CNN and GSS have good preoperative predictive accuracy for SFR. Number of implicated calices may affect SFR, and CCI affects complications. Studies should incorporate these factors in scoring systems and assess if predictability of PCNL outcomes improves.

Beam diagnostic system for SSC on HIRFL central console

International Nuclear Information System (INIS)

Zhang Guixu; Wang Zhen; Huang Tuanhua

1998-01-01

The SSC ion beam diagnostic system on the console of HIRFL in institute of modern physics is presented. The information between console and diagnostic system can be transferred via DECnet communication. The central computer for HIRFL console is VAX-8350, the working computer of diagnostic system is changed from IBM PC/XT to COMPAQ 486, and the operating program is rewritten from FORTRAN to C. In order to communicate information, DECnet TTT function is put into both programs on the VAX and PC

The Drosophila Pericentrin-like-protein (PLP cooperates with Cnn to maintain the integrity of the outer PCM

Directory of Open Access Journals (Sweden)

Jennifer H. Richens

2015-08-01

Full Text Available Centrosomes comprise a pair of centrioles surrounded by a matrix of pericentriolar material (PCM. In vertebrate cells, Pericentrin plays an important part in mitotic PCM assembly, but the Drosophila Pericentrin-like protein (PLP appears to have a more minor role in mitotic fly cells. Here we investigate the function of PLP during the rapid mitotic cycles of the early Drosophila embryo. Unexpectedly, we find that PLP is specifically enriched in the outer-most regions of the PCM, where it largely co-localizes with the PCM scaffold protein Cnn. In the absence of PLP the outer PCM appears to be structurally weakened, and it rapidly disperses along the centrosomal microtubules (MTs. As a result, centrosomal MTs are subtly disorganized in embryos lacking PLP, although mitosis is largely unperturbed and these embryos develop and hatch at near-normal rates. Y2H analysis reveals that PLP can potentially form multiple interactions with itself and with the PCM recruiting proteins Asl, Spd-2 and Cnn. A deletion analysis suggests that PLP participates in a complex network of interactions that ultimately help to strengthen the PCM.

Diagnostic system and diagnostic experiences at the Paks Nuclear Power Plant

International Nuclear Information System (INIS)

Katona, Tamas

1986-01-01

The major functions of the diagnostic system of the first two units of the Paks Nuclear Power Plant are as follows: monitoring the mechanical integrity of the reactor and the primary coolant circuit by means of vibration diagnostics; leakage detection of the primary coolant circuit by means of high frequency sonic analysis; loose parts monitoring based on the analysis of high frequency signals of acceleration detectors; and monitoring the vibration state of the turbines and rotary machines by the latter method or by a procedure based on the detection of mechanical vibrations. Up-to-date vibration diagnostics is based on the information supplied by either acceleration detectors or pressure fluctuation detectors, or in-core and ex-core neutron detectors. (V.N.)

Overview of data acquisition system for SST-1 diagnostics

International Nuclear Information System (INIS)

Sharma, Manika; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-01-01

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Overview of data acquisition system for SST-1 diagnostics

Energy Technology Data Exchange (ETDEWEB)

Sharma, Manika, E-mail: bithi@ipr.res.in; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-11-15

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Between the headlines of the Israeli - Arabic conflict: the coverage of CNN and Al Jazeera

OpenAIRE

Hemelberg, Stephany

2015-01-01

The purpose of this article is to analyze the coverage made by CNN and Al Jazeera (in Arabic) to operation Caste Lead and the Goldstone Report during 2008 and 2009. This investigation is based in the theory of Qualitative Analysis of Content, by Wildemuth and Zhang. The methodology follows up with the one proposed by the authors in the main theory, complementing it with the Gamson and Modigliani´s Framing theory. The methodology mention above display the different in the coverage development,...

A flexible simulator for training an early fault diagnostic system

International Nuclear Information System (INIS)

Marsiletti, M.; Santinelli, A.; Zuenkov, M.; Poletykin, A.

1997-01-01

An early fault diagnostic system has been developed addressed to timely trouble shooting in process plants during any operational modes. The theory of this diagnostic system is related with the usage of learning methods for automatic generation of knowledge bases. This approach enables the conversion of ''cause→effect'' relations into ''effect→possible-causes'' ones. The diagnostic rules are derived from the operation of a plant simulator according to a specific procedure. Flexibility, accuracy and high speed are the major characteristics of the training simulator, used to generate the diagnostic knowledge base. The simulator structure is very flexible, being based on LEGO code but allowing the use of practically any kind of FORTRAN routines (recently also ACSL macros has been introduced) as plant modules: this permits, when needed, a very accurate description of the malfunctions the diagnostic system should ''known''. The high speed is useful to shorten the ''learning'' phase of the diagnostic system. The feasibility of the overall system has been assessed, using as reference plant the conventional Sampierdarena (Italy) power station, that is a combined cycle plant dedicated to produce both electrical and heat power. The hardware configuration of this prototype system was made up of a network of a Hewlett-Packard workstation and a Digital VAX-Station. The paper illustrates the basic structure of the simulator used for this diagnostic system training purpose, as well as the theoretical background on which the diagnostic system is based. Some evidence of the effectiveness of the concept through the application to Sampierdarena 40 MW cogeneration plant is reported. Finally an outline of an ongoing application to a WWER-1000 plant is given; the operating system is, in this case, UNIX. (author)

An expert system for turbogenerator diagnostics

International Nuclear Information System (INIS)

Bessenyei, Z.; Tomcsanyi, T.; Toth, Z.; Laczay, I.

1992-01-01

In 1990, an expert system for turbo-generator diagnostics (EST-D) was installed at the 3rd and 4th units of the Paks NPP (Hungary). The expert system is strongly integrated to the ARGUS II vibration monitoring and diagnostics system. The system works on IBM PC AT. The VEIKI's and the NPP's human experts were interviewed to fill up the knowledgebase. The system is able to identify 13 different faults of the parts of a turbogenerator. The knowledgebase consists of ca 200 rules. The rules were built in and the system was verified and validated using a model of the turbines and using the experiences gathered with ARGUS II during the last 3 years. The maintenance personnel is authorized to modify and/or extend the knowledgebase. The input data for evaluation come from measured vibration patterns produced by the ARGUS II system, database of events, and maintenance data input by the maintenance personnel. The expert system is based on the modified GENESYS 2.1 shell (developed by SZAMALK, Hungary). Some limitations from PC application were eliminated, and a new, independent explanation module and man-machine interface were developed. Using this man-machine interface, one of the basic goals of the expert system developments was achieved: the human experts contribution is not necessary for diagnoses. The operator of the diagnostics system is able to produce the reports of diagnoses. Of course the interface allows the human experts to see the diagnoses through. It should be mentioned, at the beginning of 1991, we installed a similar expert system at the 1st 1000 MW WWER type unit of the Kalinin NPP (Soviet Union). In this paper, the operation of the EST-D, the man-machine interface and the operational experiences of the first 4 months work are explained. 2 refs., 14 figs

Clinical applications of SONIALVISION 100 digital diagnostic table system

International Nuclear Information System (INIS)

Shiomi, Takeshi; Shimizu, Tatsuya; Iinuma, Masao; Takemoto, Hajime; Tanaka, Shuji

2003-01-01

This report refers to the clinical applications of our newly developed SONIALVISION 100 fully digitalized X-ray diagnostic table system. The main design concept of the SONIALVISION 100 system is the improvement of workflow in various clinical fields. The development of digital imaging technologies has come to allow fully digitalized X-ray diagnostic table systems to be widely utilized in various clinical applications, including interventional radiology (IVR) and examinations using contrast medium. This report mainly refers to the clinical applications of the Shimadzu SONIALVISION 100 digitalized X-ray diagnostic table system, also presenting some typical image data demonstrating the high efficiency, made available through the use of this new system, in high-speed spot imaging and digital tomography. (author)

Advanced Light Source beam diagnostics systems

International Nuclear Information System (INIS)

Hinkson, J.

1993-10-01

The Advanced Light Source (ALS), a third-generation synchrotron light source, has been recently commissioned. Beam diagnostics were very important to the success of the operation. Each diagnostic system is described in this paper along with detailed discussion of its performance. Some of the systems have been in operation for two years. Others, in the storage ring, have not yet been fully commissioned. These systems were, however, working well enough to provide the essential information needed to store beam. The devices described in this paper include wall current monitors, a beam charge monitor, a 50 ohm Faraday cup, DC current transformers, broad-hand striplines, fluorescence screens, beam collimators and scrapers, and beam position monitors. Also, the means by which waveforms are digitized and displayed in the control room is discussed

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

Science.gov (United States)

Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

2017-01-01

Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

RANZAR Body Systems Framework of diagnostic imaging examination descriptors

International Nuclear Information System (INIS)

Pitman, Alexander D.; Penlington, Lisa; Doromal, Darren; Vukolova, Natalia; Slater, Gregory

2014-01-01

A unified and logical system of descriptors for diagnostic imaging examinations and procedures is a desirable resource for radiology in Australia and New Zealand and is needed to support core activities of RANZCR. Existing descriptor systems available in Australia and New Zealand (including the Medicare DIST and the ACC Schedule) have significant limitations and are inappropriate for broader clinical application. An anatomically based grid was constructed, with anatomical structures arranged in rows and diagnostic imaging modalities arranged in columns (including nuclear medicine and positron emission tomography). The grid was segregated into five body systems. The cells at the intersection of an anatomical structure row and an imaging modality column were populated with short, formulaic descriptors of the applicable diagnostic imaging examinations. Clinically illogical or physically impossible combinations were ‘greyed out’. Where the same examination applied to different anatomical structures, the descriptor was kept identical for the purposes of streamlining. The resulting Body Systems Framework of diagnostic imaging examination descriptors lists all the reasonably common diagnostic imaging examinations currently performed in Australia and New Zealand using a unified grid structure allowing navigation by both referrers and radiologists. The Framework has been placed on the RANZCR website and is available for access free of charge by registered users. The Body Systems Framework of diagnostic imaging examination descriptors is a system of descriptors based on relationships between anatomical structures and imaging modalities. The Framework is now available as a resource and reference point for the radiology profession and to support core College activities.

RANZCR Body Systems Framework of diagnostic imaging examination descriptors.

Science.gov (United States)

Pitman, Alexander G; Penlington, Lisa; Doromal, Darren; Slater, Gregory; Vukolova, Natalia

2014-08-01

A unified and logical system of descriptors for diagnostic imaging examinations and procedures is a desirable resource for radiology in Australia and New Zealand and is needed to support core activities of RANZCR. Existing descriptor systems available in Australia and New Zealand (including the Medicare DIST and the ACC Schedule) have significant limitations and are inappropriate for broader clinical application. An anatomically based grid was constructed, with anatomical structures arranged in rows and diagnostic imaging modalities arranged in columns (including nuclear medicine and positron emission tomography). The grid was segregated into five body systems. The cells at the intersection of an anatomical structure row and an imaging modality column were populated with short, formulaic descriptors of the applicable diagnostic imaging examinations. Clinically illogical or physically impossible combinations were 'greyed out'. Where the same examination applied to different anatomical structures, the descriptor was kept identical for the purposes of streamlining. The resulting Body Systems Framework of diagnostic imaging examination descriptors lists all the reasonably common diagnostic imaging examinations currently performed in Australia and New Zealand using a unified grid structure allowing navigation by both referrers and radiologists. The Framework has been placed on the RANZCR website and is available for access free of charge by registered users. The Body Systems Framework of diagnostic imaging examination descriptors is a system of descriptors based on relationships between anatomical structures and imaging modalities. The Framework is now available as a resource and reference point for the radiology profession and to support core College activities. © 2014 The Royal Australian and New Zealand College of Radiologists.

On-line diagnostics for a real time system

International Nuclear Information System (INIS)

Sreenivasan, P.

1976-01-01

The purpose of an on-line diagnostics is to infuse the ability of self diagnosing in an online computer to enhance its dependability in a real time system. Such a diagnostics evolved for the CDPS of the Fast Breeder Test Reactor at Kalpakkam is reported. The two phases of the diagnostics, i.e., the malfunction detection and post detection action are described in some detail. (A.K.)

System theory in medical diagnostic devices: an overview.

Science.gov (United States)

Baura, Gail D

2006-01-01

Medical diagnostics refers to testing conducted either in vitro or in vivo to provide critical health care information for risk assessment, early diagnosis, treatment, or disease management. Typical in vivo diagnostic tests include the computed tomography scan, magnetic resonance imaging, and blood pressure screening. Typical in vitro diagnostic tests include cholesterol, Papanicolaou smear, and conventional glucose monitoring tests. Historically, devices associated with both types of diagnostics have used heuristic curve fitting during signal analysis. However, since the early 1990s, a few enterprising engineers and physicians have used system theory to improve their core processing for feature detection and system identification. Current applications include automated Pap smear screening for detection of cervical cancer and diagnosis of Alzheimer's disease. Future applications, such as disease prediction before symptom onset and drug treatment customization, have been catalyzed by the Human Genome Project.

Effective diagnostic DAQ systems to reduce unnecessary data in KSTAR

Energy Technology Data Exchange (ETDEWEB)

Lee, Taegu, E-mail: glory@nfri.re.kr; Lee, Woongryol; Hong, Jaesic; Park, Kaprai

2016-11-15

Highlights: • When plasma shots do not successfully perform during the intended target time, the diagnostics systems continue to record these unusable data, contributing to increasing data size. • To overcome this problem, some KSTAR’s library were upgraded to monitor the plasma status in real-time. • With the real-time information of plasma status, some of the KSTAR diagnostic systems stop the acquisition process of unnecessary data. • We were able to reduce the refuse data of approximately 698 GByte in the KSTAR 7th campaign. • It was a very effective way to store useful data, and it was helpful to analysts after shot. - Abstract: The plasma status of Korea Superconducting Tokamak Advanced Research (KSTAR) is measured by various diagnostics systems. The measured data size has been increasing every year due to increasing plasma pulse lengths, higher diagnostics operating frequencies, the additions of new diagnostic systems, and an increasing number of diagnostics channels. At times, when plasma shots do not successfully perform during the intended target time, the diagnostics systems continue to record these unusable data, contributing to increasing data size. In addition, the analysis time was affected, as these data need to be separated from the relevant data set. To overcome this problem, KSTAR’s Standard Framework (SFW), Real Time Monitoring (RTMON), and Pulse Automation and Scheduling System (PASS) were upgraded to monitor the plasma status in real-time. When the plasma current is less than 200kA, RTMON sends the plasma status information every second to the SFW via EPICS Channel Access. With the real-time information on plasma status, some of the KSTAR diagnostic systems stop the acquisition process of unnecessary data. This paper describes a method for reducing the storage of unnecessary data and its results in the KSTAR 7th campaign.

Development of the Model of the System of Managerial Diagnostics of the Enterprise on the Basis of Improvement of Diagnostic Purposes

Directory of Open Access Journals (Sweden)

Grzegorz Pawlowski

2017-11-01

Full Text Available The purpose of the article is to develop a model of the system of managerial diagnostics of the enterprise on the basis of the improvement of diagnostic purposes. The developed model of the system of managerial diagnostics of the enterprise is a set of subjects (owners, managers, investors, specialists, etc., objects (management system, resources, technology, methods (a set of methods and means, business indicators and criteria (parameters that, when interacting, provide the achievement (efficient and effective of the diagnostic objectives of the system of the objectives of managerial diagnostics of the enterprise, taking into account the compliance of its competitive strategy of the state of the environment function of direct action (competitors, customers, suppliers, mediators, and other contact audiences in the context of improving the efficiency and developing the management. It is determined that the system of goals of the model of the system of managerial diagnostics of the enterprise (taking into account the ensuring of the compliance of the system of management with strategic goals and tactical tasks form the following key diagnostic objectives that require improvement on the basis of business indicators (parameters, namely: 1 diagnostics of the effectiveness of controlling the internal business processes of the enterprise; 2 diagnostics of the effectiveness of the typical organizational structure of enterprise management; 3 diagnostics of the efficiency of standardization of the work of linear and functional managers and specialists at the enterprise; 4 diagnostics of the enterprise in the areas of vocational education, labor activity and motivation, innovation work and social development; 5 diagnostics of the level of conflict in the team at the enterprise; 6 diagnostics of efficiency of use of information technologies in the management of the enterprise. The prospect of further research in this area is to improve the complex system of

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

Science.gov (United States)

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images

Detection of vehicle parts based on Faster R-CNN and relative position information

Science.gov (United States)

Zhang, Mingwen; Sang, Nong; Chen, Youbin; Gao, Changxin; Wang, Yongzhong

2018-03-01

Detection and recognition of vehicles are two essential tasks in intelligent transportation system (ITS). Currently, a prevalent method is to detect vehicle body, logo or license plate at first, and then recognize them. So the detection task is the most basic, but also the most important work. Besides the logo and license plate, some other parts, such as vehicle face, lamp, windshield and rearview mirror, are also key parts which can reflect the characteristics of vehicle and be used to improve the accuracy of recognition task. In this paper, the detection of vehicle parts is studied, and the work is novel. We choose Faster R-CNN as the basic algorithm, and take the local area of an image where vehicle body locates as input, then can get multiple bounding boxes with their own scores. If the box with maximum score is chosen as final result directly, it is often not the best one, especially for small objects. This paper presents a method which corrects original score with relative position information between two parts. Then we choose the box with maximum comprehensive score as the final result. Compared with original output strategy, the proposed method performs better.

Diagnostic and Measuring Systems of the Power Transformers

Directory of Open Access Journals (Sweden)

Jan Michalik

2003-01-01

Full Text Available In the article the diagnostic and measuring systems dedicated for complex output tests of power transformers aswell as their diagnostic is dcscribcd. The aim of research in this area was to elaborate the problem of so-called open loop measuring system controlled by PC. The attribute "open" means the possibility to adapt the system for different electric equipment, different measurands and an zdaptation of the way of monitoring, evaluation and distribution of output information according to specific requirements the controlled transformer.

ISHM-oriented adaptive fault diagnostics for avionics based on a distributed intelligent agent system

Science.gov (United States)

Xu, Jiuping; Zhong, Zhengqiang; Xu, Lei

2015-10-01

In this paper, an integrated system health management-oriented adaptive fault diagnostics and model for avionics is proposed. With avionics becoming increasingly complicated, precise and comprehensive avionics fault diagnostics has become an extremely complicated task. For the proposed fault diagnostic system, specific approaches, such as the artificial immune system, the intelligent agents system and the Dempster-Shafer evidence theory, are used to conduct deep fault avionics diagnostics. Through this proposed fault diagnostic system, efficient and accurate diagnostics can be achieved. A numerical example is conducted to apply the proposed hybrid diagnostics to a set of radar transmitters on an avionics system and to illustrate that the proposed system and model have the ability to achieve efficient and accurate fault diagnostics. By analyzing the diagnostic system's feasibility and pragmatics, the advantages of this system are demonstrated.

Towards intelligent diagnostic system employing integration of mathematical and engineering model

International Nuclear Information System (INIS)

Isa, Nor Ashidi Mat

2015-01-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

Towards intelligent diagnostic system employing integration of mathematical and engineering model

Science.gov (United States)

Isa, Nor Ashidi Mat

2015-05-01

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

Towards intelligent diagnostic system employing integration of mathematical and engineering model

Energy Technology Data Exchange (ETDEWEB)

Isa, Nor Ashidi Mat [Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang (Malaysia)

2015-05-15

The development of medical diagnostic system has been one of the main research fields during years. The goal of the medical diagnostic system is to place a nosological system that could ease the diagnostic evaluation normally performed by scientists and doctors. Efficient diagnostic evaluation is essentials and requires broad knowledge in order to improve conventional diagnostic system. Several approaches on developing the medical diagnostic system have been designed and tested since the earliest 60s. Attempts on improving their performance have been made which utilizes the fields of artificial intelligence, statistical analyses, mathematical model and engineering theories. With the availability of the microcomputer and software development as well as the promising aforementioned fields, medical diagnostic prototypes could be developed. In general, the medical diagnostic system consists of several stages, namely the 1) data acquisition, 2) feature extraction, 3) feature selection, and 4) classifications stages. Data acquisition stage plays an important role in converting the inputs measured from the real world physical conditions to the digital numeric values that can be manipulated by the computer system. One of the common medical inputs could be medical microscopic images, radiographic images, magnetic resonance image (MRI) as well as medical signals such as electrocardiogram (ECG) and electroencephalogram (EEG). Normally, the scientist or doctors have to deal with myriad of data and redundant to be processed. In order to reduce the complexity of the diagnosis process, only the significant features of the raw data such as peak value of the ECG signal or size of lesion in the mammogram images will be extracted and considered in the subsequent stages. Mathematical models and statistical analyses will be performed to select the most significant features to be classified. The statistical analyses such as principal component analysis and discriminant analysis as well

Automated System for Control of the Vacuum Diagnostic System for the TJ-II

International Nuclear Information System (INIS)

Lopez Sanchez, A.; Montoro Peinado, A.; Encabo Fernandez, J.; Gama de la Serrano, J.; Sanchez Sarabia, E.

1999-12-01

This report describes the monitoring and remote control systems belonging to the high vacuum systems of the TJ-II diagnostics. These systems are part of each diagnostic and their control has been integrated into the automata that carries out this task. All the controllers are connected through a Profibus network, so as to interchange data between themselves as well as between the general system of TJ-II. (Author)

Local area network for the plasma diagnostics system of MFTF-B

International Nuclear Information System (INIS)

Lau, N.H.; Minor, E.G.

1983-01-01

The MFTF-B Plasma Diagnostics System will be implemented in stages, beginning with a start-up set of diagnostics and evolving toward a basic set. The start-up set contains 12 diagnostics which will acquire a total of about 800 Kbytes of data per machine pulse; the basic set contains 23 diagnostics which will acquire a total of about 8 Mbytes of data per pulse. Each diagnostic is controlled by a Foundation System consisting of a DEC LSI-11/23 microcomputer connected to CAMAC via a 5 Mbits/second serial fiber-optic link and connected to a supervisory computer (Perkin-Elmer 3250) via a 9600 baud RS232 link. The Foundation System is a building block used throughout MFTF-B for control and status monitoring. However, its 9600 baud link to the supervisor presents a bottleneck for the large data transfers required by diagnostics. To overcome this bottleneck the diagnostics Foundation Systems will be connected together with an additional LSI-11/23 called the master to form a Local Area Network (LAN) for data acquisition

Laser and plasma diagnostics for the OMEGA Upgrade Laser System (invited) (abstract)

International Nuclear Information System (INIS)

Letzring, S.A.

1995-01-01

The upgraded OMEGA laser system will be capable of delivering up to 30 kJ of 351-nm laser light with various temporal pulse shapes onto a variety of targets for both ICF and basic plasma physics experiments. ICF experiments will cover a wide parameter space up to near-ignition conditions, and basic interaction and plasma physics experiments will cover previously unattainable parameter spaces. The laser system is the tool with which the experiments are performed; the diagnostics, both of the laser system and the interaction between the laser and the target, form the heart of the experiment. A new suite of diagnostics is now being designed and constructed. Most of these are based on diagnostics previously fielded on the OMEGA laser system very successfully over the last ten years, but there are some new diagnostics, both for the laser and the interaction experiments, which have had to be invented. Laser system diagnostics include high-energy, full-beam calorimetry for all of the 60 beams of the upgrade; a novel, multispectral energy-measuring system for assessing the tuning of the frequency-multiplying crystals; a beam-balance diagnostic that forms the heart of the energy-balance system; and a peak power diagnostic that forms the heart of the power-balance system. Target diagnostics will include the usual time-integrated x-ray imaging systems, both pinhole cameras and x-ray microscopes; x-ray spectrometers, both imaging and spatially integrating; plamsa calorimeters, including x-ray calorimetry; and time-resolved x-ray diagnostics, both nonimaging and imaging in one and two dimensions. Neutron diagnostics will include several measurements of total yield, secondary, and possibly tertiary yield and neutron spectroscopy with several time-of-flight spectrometers. Other measurements will include ''knock-on'' particle measurements and neutron activation of shell materials as a diagnostic of compressed fuel and shell density

Integration of the ITER diagnostic plant systems with CODAC

International Nuclear Information System (INIS)

Simrock, S.; Barnsley, R.; Bertalot, L.; Hansalia, C.; Klotz, W.D.; Makijarvi, P.; Reichle, R.; Vayakis, G.; Yonekawa, I.; Walker, C.; Wallander, A.; Walsh, M.; Winter, A.

2011-01-01

ITER requires extensive diagnostic systems in order to meet the requirements for machine operation, protection, plasma control and physics studies. The realization of these systems is a major challenge not only because of the harsh environment and the nuclear requirements but also with respect to Instrumentation and Control (I and C) of all the 59 diagnostics plants. The Plant Systems I and C are mostly 'in-kind', i.e. procured by the seven ITER Domestic Agencies (DAs), while the Central I and C Systems are 'in-fund', i.e. procured by ITER Organization (IO). Standardization of Plant Systems I and C is of primary importance and has been one of the highest priority tasks of CODAC. The standards are published in the Plant Control Design Handbook (PCDH) which will be followed to ensure a homogeneous design, guarantee high availability and simplify maintenance and support future upgrades. Most important for a successful commissioning and operation of the ITER facility are the concepts of interfacing the diagnostics plant systems with CODAC and the standards for instrumentation and control which must be followed all contributing parties. In this paper, we will elaborate on the concepts of interfacing the diagnostics plant systems with CODAC and the standards that must be followed for the design.

An investigation into the use of ''expert systems'' for system-wide diagnostics

International Nuclear Information System (INIS)

Booth, A.W.; Carroll, J.T.

1987-01-01

This paper has explained how expert systems function and how they might be used to provide a FASTBUS system-wide diagnostic program. The authors propose that the system be used to diagnose the FASTBUS system at FERMILAB's CDF experiment. There are many important areas which have not been addressed in great detail in this paper (such as the roles of the knowledge engineer and the expert during the knowledge acquisition phase), but the central idea of the embodiment of an expert skill in a computer is clear. Development of a system-wide diagnostic program requires building knowledge from all our system experts, into the system. To expand the expert system beyond its network diagnostic ability, to include finding faulty modules would be worthwhile. Having an ''intelligent'' assistant who is on shift 24 hours each day would relieve the ''real'' experts from laborious, time-consuming and sometimes repetitive tasks undertaken during the debugging process. The system could also provide a testbed for evaluation and comparison when considering future expert-system applications such as ''run-control'' and ''data analysis''. In the context of a system-wide diagnostic program, an ''expert system'' is not intended to replace human experts but simply to help them. It is envisaged that there will always be important interaction between the human expert and the ''expert system''. The incremental development of the ''expert system'' should ensure that it is useful in the short term (by debugging to the S.I./segment level for example), and even more useful in the medium to longer term as it acquires more and more knowledge and the ability to debug to the module level. Expert systems exist and are working successfully in many problem domains. See the bibliography for examples of ''expert systems'' built in the high energy physics environment

Performance diagnostic system for emergency diesel generators

International Nuclear Information System (INIS)

Logan, K.P.

1991-01-01

Diesel generators are commonly used for emergency backup power at nuclear stations. Emergency diesel generators (EDGs) are subject to both start-up and operating failures, due to infrequent and fast-start use. EDG reliability can be critical to plant safety, particularly when station blackout occurs. This paper describes an expert diagnostic system designed to consistently evaluate the operating performance of diesel generators. The prototype system is comprised of a suite of sensor monitoring, cylinder combustion analyzing, and diagnostic workstation computers. On-demand assessments of generator and auxiliary equipment performance are provided along with color trend displays comparing measured performance to reference-normal conditions

Diagnostics systems for the TBR-E tokamak

International Nuclear Information System (INIS)

Ueda, M.; Ferreira, J.L.; Aso, Y.; Ferreira, J.G.

1992-08-01

A general view of the several diagnostics systems proposed for the TBR-E tokamak is given. This project is a joint undertaking of INPE, USP and UNICAMP plasma laboratories. The requirements for the measurements of the plasma produced parameters are described. Special attention is given for diagnostics used to investigate new physical issues on a low aspect ratio tokamak such as TBR-E. (author)

Target Diagnostic Control System Implementation for the National Ignition Facility

International Nuclear Information System (INIS)

Shelton, R.T.; Kamperschroer, J.H.; Lagin, L.J.; Nelson, J.R.; O'Brien, D.W.

2010-01-01

The extreme physics of targets shocked by NIF's 192-beam laser are observed by a diverse suite of diagnostics. Many diagnostics are being developed by collaborators at other sites, but ad hoc controls could lead to unreliable and costly operations. A Diagnostic Control System (DCS) framework for both hardware and software facilitates development and eases integration. Each complex diagnostic typically uses an ensemble of electronic instruments attached to sensors, digitizers, cameras, and other devices. In the DCS architecture each instrument is interfaced to a low-cost Windows XP processor and Java application. Each instrument is aggregated with others as needed in the supervisory system to form an integrated diagnostic. The Java framework provides data management, control services and operator GUI generation. DCS instruments are reusable by replication with reconfiguration for specific diagnostics in XML. Advantages include minimal application code, easy testing, and high reliability. Collaborators save costs by assembling diagnostics with existing DCS instruments. This talk discusses target diagnostic instrumentation used on NIF and presents the DCS architecture and framework.

The wireless diagnostic system for motor operated valves

International Nuclear Information System (INIS)

Ito, Haruo; Akiyama, Michiaki; Suzuki, Syunichi

2010-01-01

To aim at maintenance optimization, a motor operated valve (MOV) diagnostic system called 'MOVDAS' has been developed by using new sensor technologies incorporating torque sensor into the MOV. It has been introduced into nuclear power plants operated by Japan Atomic Power Company (JAPC) for the support of Condition Based Maintenance (CBM). This system, directly checking the torque behavior of the MOV, accurately diagnoses the condition of the MOV during plant operation. Further for the ease of data collection and manpower saving, the wireless diagnostic system based on MOVDAS utilizing Personal Handyphone System (PHS) has been recently introduced into nuclear power plants in JAPC. (author)

Comparative guide to emerging diagnostic tools for large commercial HVAC systems

Energy Technology Data Exchange (ETDEWEB)

Friedman, Hannah; Piette, Mary Ann

2001-05-01

This guide compares emerging diagnostic software tools that aid detection and diagnosis of operational problems for large HVAC systems. We have evaluated six tools for use with energy management control system (EMCS) or other monitoring data. The diagnostic tools summarize relevant performance metrics, display plots for manual analysis, and perform automated diagnostic procedures. Our comparative analysis presents nine summary tables with supporting explanatory text and includes sample diagnostic screens for each tool.

Systems for noise diagnostics of WWER nuclear power plants

International Nuclear Information System (INIS)

Por, G.

1996-01-01

The aim of this paper is to give a short overview of the noise diagnostics system developed by Hungarian firms which are in operation in WWER type NPP Units. Giving a list of systems developed for noise diagnostics of WWER reactors we present their main characteristics, their goal and some of their achievements. The second part deals with the problem of acceptance of noise system by NPP and regulations. (author). 24 refs

Systems for noise diagnostics of WWER nuclear power plants

Energy Technology Data Exchange (ETDEWEB)

Por, G [Technical Univ. of Budapest, Budapest (Hungary)

1997-12-31

The aim of this paper is to give a short overview of the noise diagnostics system developed by Hungarian firms which are in operation in WWER type NPP Units. Giving a list of systems developed for noise diagnostics of WWER reactors we present their main characteristics, their goal and some of their achievements. The second part deals with the problem of acceptance of noise system by NPP and regulations. (author). 24 refs.

Diagnostic systems developed in NPPRI (VUJE) Trnava Inc. for NPPs

International Nuclear Information System (INIS)

Kucharek, P.

1998-01-01

Since foundation of Nuclear Power Plant Research Institute (NPPRI) in 1977, the department of diagnostics has been dealt with problems related to the theoretical, practical and organizatory questions of operational diagnostics connected with PWR type nuclear components. This department acts directly in locality of NPP Jaslovske Bohunice, but there are performances for all NPP in Slovak or Czech Republic (Dukovany, Mochovce, and Temelin). Besides direct services and achievements for NPP there exist advisory, experts and research activities for the government and supervising authorities, too. In 1985, NPPRI began systematically construct and verify technical means for operational diagnostics of main circulating pumps (MCP) with good results, based on own rich practical experiences and contacts with organisations abroad. In recent years NPPRI as one of recognised qualified and authorised institutions in Slovak Republic has begun to develop a new generation of diagnostic systems for NPP on high technical level but with lower procuring costs in comparison with western countries products. This contribution deals with four following types of diagnostic systems which were not only developed but also delivered and installed on Slovak and Czech nuclear units: - Loose part monitoring system (LPMS), - Humidity monitoring system (HUMON), - Reactor coolant pumps monitoring system (RCPMS), - Primary circuit vibration monitoring system (VMS). Main features of new generation from middle of 1990's of these systems are described in this paper and operational experiences with them too. (author)

Overview of MFTF supervisory control and diagnostics system software

International Nuclear Information System (INIS)

Ng, W.C.

1979-01-01

The Mirror Fusion Test Facility (MFTF) at the Lawrence Livermore Laboratory (LLL) is currently the largest mirror fusion research project in the world. Its Control and Diagnostics System is handled by a distributed computer network consisting of nine Interdata minicomputer systems and about 65 microprocessors. One of the design requirements is tolerance of single-point failure. If one of the computer systems becomes inoperative, the experiment can still be carried out, although the system responsiveness to operator command may be degraded. In a normal experiment cycle, the researcher can examine the result of the previous experiment, change any control parameter, fire a shot, collect four million bytes of diagnostics data, perform intershot analysis, and have the result presented - all within five minutes. The software approach adopted for the Supervisory Control and Diagnostics System features chief programmer teams and structured programming. Pascal is the standard programming language in this project

Dynamic Visualization of SNS Diagnostics Summary Report and System Status

CERN Document Server

Blokland, Willem; Long, Cary D; Murphy, Darryl J; Purcell, John D; Sundaram, Madhan

2005-01-01

The Spallation Neutron Source (SNS) accelerator systems will deliver a 1.0 GeV, 1.4 MW proton beam to a liquid mercury target for neutron scattering research. The accelerator complex consists of a 1 GeV linear accelerator, an accumulator ring and associated transport lines. The SNS diagnostics platform is PC-based running Embedded Windows XP and LabVIEW. The diagnostics instruments communicate with the control system using the Channel Access (CA) protocol of the Experimental Physics and Industrial Control System (EPICS). This paper describes the Diagnostics Group's approach to collecting data from the instruments, processing it, and presenting live in a summarized way over the web. Effectively, adding a supervisory level to the diagnostics instruments. One application of this data mining is the "Diagnostics Status Page" that summarizes the insert-able devices, transport efficiencies, and the mode of the accelerator in a compact webpage. The displays on the webpage change automatically to show the latest and/o...

[Development of the lung cancer diagnostic system].

Science.gov (United States)

Lv, You-Jiang; Yu, Shou-Yi

2009-07-01

To develop a lung cancer diagnosis system. A retrospective analysis was conducted in 1883 patients with primary lung cancer or benign pulmonary diseases (pneumonia, tuberculosis, or pneumonia pseudotumor). SPSS11.5 software was used for data processing. For the relevant factors, a non-factor Logistic regression analysis was used followed by establishment of the regression model. Microsoft Visual Studio 2005 system development platform and VB.Net corresponding language were used to develop the lung cancer diagnosis system. The non-factor multi-factor regression model showed a goodness-of-fit (R2) of the model of 0.806, with a diagnostic accuracy for benign lung diseases of 92.8%, a diagnostic accuracy for lung cancer of 89.0%, and an overall accuracy of 90.8%. The model system for early clinical diagnosis of lung cancer has been established.

The JET diagnostic fast central acquisition and trigger system (abstract)

Science.gov (United States)

Edwards, A. W.; Blackler, K.

1995-01-01

Most plasma physics diagnostics sample at a fixed frequency that is normally matched to available memory limits. This technique is not appropriate for long pulse machines such as JET where sampling frequencies of hundreds of kHz are required to diagnose very fast events. As a result of work using real-time event selection within the previous JET soft x-ray diagnostic, a single data acquisition and event triggering system for all suitable fast diagnostics, the fast central acquisition and trigger system (Fast CATS), has been developed for JET. The front-end analog-to-digital conversion (ADC) part samples all channels at 250 kHz, with a 100 kHz pass band and a stop band of 125 kHz. The back-end data collection system is based around Texas Instruments TMS320C40 microprocessors. Within this system, two levels of trigger algorithms are able to evaluate data. The first level typically analyzes data on a per diagnostic and individual channel basis. The second level looks at the data from one or more diagnostics in a window around the time of interest flagged by the first level system. Selection criteria defined by the diagnosticians are then imposed on the results from the second level to decide whether that data should be kept. The use of such a system involving intelligent real time trigger algorithms and fast data analysis will improve both the quantity and quality of JET diagnostic data, while providing valuable input to the design of data acquisition systems for very long pulse machines such as ITER. This paper will give an overview of the various elements of this new system. In addition, first results from this system following the restart of JET operation will be presented.

Thioaptamer Diagnostic System (TDS)

Science.gov (United States)

Yang, Xianbin

2015-01-01

AM Biotechnologies, LLC, in partnership with Sandia National Laboratories, has developed a diagnostic device that quickly detects sampled biomarkers. The TDS quickly quantifies clinically relevant biomarkers using only microliters of a single sample. The system combines ambient-stable, long shelf-life affinity assays with handheld, microfluidic gel electrophoresis affinity assay quantification technology. The TDS is easy to use, operates in microgravity, and permits simultaneous quantification of 32 biomarkers. In Phase I of the project, the partners demonstrated that a thioaptamer assay used in the microfluidic instrument could quantify a specific biomarker in serum in the low nanomolar range. The team also identified novel affinity agents to bone-specific alkaline phosphatase (BAP) and demonstrated their ability to detect BAP with the microfluidic instrument. In Phase II, AM Biotech expanded the number of ambient affinity agents and demonstrated a TDS prototype. In the long term, the clinical version of the TDS will provide a robust, flight-tested diagnostic capability for space exploration missions.

Design and integration of lower ports for ITER diagnostic systems

Energy Technology Data Exchange (ETDEWEB)

Casal, Natalia, E-mail: Natalia.casal@iter.org [ITER Organization, Route de Vinon-sur-Verdon – CS 90 046 – 13067 St Paul Lez Durance Cedex (France); Bertalot, Luciano; Cheng, Hao; Drevon, Jean Marc; Duckworth, Philip; Giacomin, Thibaud; Guirao, Julio; Iglesias, Silvia [ITER Organization, Route de Vinon-sur-Verdon – CS 90 046 – 13067 St Paul Lez Durance Cedex (France); Kochergin, Mikhail [IOFFE Institute, Saint Petersburg (Russian Federation); Martin, Alex [ITER Organization, Route de Vinon-sur-Verdon – CS 90 046 – 13067 St Paul Lez Durance Cedex (France); McCarron, Eddie [Oxford Technologies Ltd., Abingdon (United Kingdom); Mokeev, Alexander [Russian Federation Domestic Agency, Moscow (Russian Federation); Mota, Fernando [CIEMAT, Madrid (Spain); Penot, Christophe; Portales, Mickael [ITER Organization, Route de Vinon-sur-Verdon – CS 90 046 – 13067 St Paul Lez Durance Cedex (France); Kitazawa, Sin-iti [Japanese Domestic Agency, Naka (Japan); Sky, Jack [Oxford Technologies Ltd., Abingdon (United Kingdom); Suarez, Alejandro; Udintsev, Victor; Utin, Yuri [ITER Organization, Route de Vinon-sur-Verdon – CS 90 046 – 13067 St Paul Lez Durance Cedex (France); and others

2015-10-15

Highlights: • Lower port structures are in its conceptual design phase. • Electromagnetic and seismic loads, will dominate all other mechanical loads. • Design allows diagnostics support, neutron shielding while and signals transmission. • Installation and maintenance operations are fully remote handling compatible. - Abstract: All around the ITER vacuum vessel, forty-four ports will provide access to the vacuum vessel for remote handling operations, diagnostic systems, heating, and vacuum systems: 18 upper ports, 17 equatorial ports, and 9 lower ports. Among the lower ports, three of them will be used for the remote handling installation of the ITER divertor. Once the divertor is in place, these ports will host various diagnostic systems mounted in the so-called diagnostic racks. The diagnostic racks must allow the support and cooling of the diagnostics, extraction of the required diagnostic signals, and providing access and maintainability while minimizing the leakage of radiation toward the back of the port where the humans are allowed to enter. A fully integrated inner rack, carrying the near plasma diagnostic components, will be an stainless steel structure, 4.2 m long, with a maximum weight of 10 t. This structure brings water for cooling and baking at maximum temperature of 240 °C and provides connection with gas, vacuum and electric services. Additional racks (placed away from plasma and not requiring cooling) may be required for the support of some particular diagnostic components. The diagnostics racks and its associated ex vessel structures, which are in its conceptual design phase, are being designed to survive the lifetime of ITER of 20 years. This paper presents the current state of development including interfaces, diagnostic integration, operation and maintenance, shielding requirements, remote handling, loads cases and discussion of the main challenges coming from the severe environment and engineering requirements.

Online monitoring and diagnostic system on RA-6 nuclear reactor

International Nuclear Information System (INIS)

Garcia Peyrano, O. A.; Marticorena, M.; Koch, R. G.; Martinez, J. S; Berruti, G. E.; Nunez, W. M.; Gonzales, L. A.; Tarquini, L. D.; Sotelo, J. P

2009-01-01

This paper presents the Online Automatic Monitoring and Diagnostic System for mechanical components, installed on RA-6 Nuclear Reactor (San Carlos de Bariloche, Argentina). This system has been designed, installed and set-up by the Vibrations and Mechatronics Laboratory (Centro Atomico Bariloche, Comision Nacional de Energia Atomica) and Sitrack.com Argentina SA. This system provides an online mechanical diagnostic of the main reactor components, allowing incipient failures to be early detected and identified, avoiding unscheduled shut-downs and reducing maintenance times. The diagnostic is accomplished by an online analysis of the vibratory signature of the mechanical components, obtained by vibrations sensors on the main pump and the decay tank. The mechanical diagnostic and the main operational parameters are displayed on the reactor control room and published on the internet. [es

The local area network for the plasma Diagnostics System of MFTF-B

International Nuclear Information System (INIS)

Lau, N.H.; Minor, E.G.

1983-01-01

The MFTF-B Plasma Diagnostics System will be implemented in stages, beginning with a start-up set of diagnostics and evolving toward a basic set. The start-up set contains 12 diagnostics which will acquire a total of about 800 Kbytes of data per machine pulse; the basic set contains 23 diagnostics which will acquire a total of about 8 Mbytes of data per pulse. Each diagnostic is controlled by a ''Foundation System'' consisting of a DEC LSI-11/23 microcomputer connected to CAMAC via a 5 Mbits/second serial fiber-optic link and connected to a supervisory computer (Perkin-Elmer 3250) via a 9600 baud RS232 link. The Foundation System is a building block used throughout MFTF-B for control and status monitoring. However, its 9600 baud link to the supervisor presents a bottleneck for the large data transfers required by diagnostics. To overcome this bottleneck the diagnostics Foundation Systems will be connected together with an additional LSI-11/23 called the ''master'' to form a Local Area Network (LAN) for data acquisition. The Diagnostics LAN has a ring architecture with token passing arbitration

Application of diagnostic system for diesel engines in nuclear power plant

International Nuclear Information System (INIS)

Yoshinaga, Takeshi

2004-01-01

The diagnostic system for diesel engines makes a diagnosis of secular change and abnormal indications of diesel engines (DG) by combination of characteristic analysis of engine, lubricating oil, fuel oil, and cooling water. The principles of diagnostic system for DG, results of confirmation of the efficiency and the maintenance plan for DG in the Japan Atomic Power Company are described. DG in the company is classified to a safety device in order to supply the power source to the Emergency Core Cooling System etc., when the power source in the plant is lost, for example, at lightning struck. Characteristics of DG, outline of the diagnostic system for DG, diagnostic technologies such as engine signature analysis, chemical analysis of samples, temperature measurement, degradation mode of DG, and training in the company are stated. (S.Y.)

Beam Diagnostics Systems for the National Ignition Facility

International Nuclear Information System (INIS)

Demaret, R D; Boyd, R D; Bliss, E S; Gates, A J; Severyn, J R

2001-01-01

The National Ignition Facility (NIF) laser focuses 1.8 megajoules of ultraviolet light (wavelength 351 nanometers) from 192 beams into a 600-micrometer-diameter volume. Effective use of this output in target experiments requires that the power output from all of the beams match within 8% over their entire 20-nanosecond waveform. The scope of NIF beam diagnostics systems necessary to accomplish this task is unprecedented for laser facilities. Each beamline contains 110 major optical components distributed over a 510-meter path, and diagnostic tolerances for beam measurement are demanding. Total laser pulse energy is measured with 2.8% precision, and the interbeam temporal variation of pulse power is measured with 4% precision. These measurement goals are achieved through use of approximately 160 sensor packages that measure the energy at five locations and power at three locations along each beamline using 335 photodiodes, 215 calorimeters, and 36 digitizers. Successful operation of such a system requires a high level of automation of the widely distributed sensors. Computer control systems provide the basis for operating the shot diagnostics with repeatable accuracy, assisted by operators who oversee system activities and setup, respond to performance exceptions, and complete calibration and maintenance tasks

Video integrated measurement system. [Diagnostic display devices

Energy Technology Data Exchange (ETDEWEB)

Spector, B.; Eilbert, L.; Finando, S.; Fukuda, F.

1982-06-01

A Video Integrated Measurement (VIM) System is described which incorporates the use of various noninvasive diagnostic procedures (moire contourography, electromyography, posturometry, infrared thermography, etc.), used individually or in combination, for the evaluation of neuromusculoskeletal and other disorders and their management with biofeedback and other therapeutic procedures. The system provides for measuring individual diagnostic and therapeutic modes, or multiple modes by split screen superimposition, of real time (actual) images of the patient and idealized (ideal-normal) models on a video monitor, along with analog and digital data, graphics, color, and other transduced symbolic information. It is concluded that this system provides an innovative and efficient method by which the therapist and patient can interact in biofeedback training/learning processes and holds considerable promise for more effective measurement and treatment of a wide variety of physical and behavioral disorders.

Shiva and Argus target diagnostics vacuum systems

International Nuclear Information System (INIS)

Glaros, S.S.; Mayo, S.E.; Campbell, D.; Holeman, D.

1978-09-01

The normal operation of LLL's Argus and Shiva laser irradiation facilities demand a main vacuum system for the target chamber and a separate local vacuum system for each of the larger appendage dianostics. This paper will describe the Argus and Shiva main vacuum systems, their respective auxiliary vacuum systems and the individual diagnostics with their respective special vacuum requirements and subsequent vacuum systems. Our latest approach to automatic computer-controlled vacuum systems will be presented

Vacuum system design and tritium inventory for the TFTR charge exchange diagnostic

International Nuclear Information System (INIS)

Medley, S.S.

1979-05-01

The charge exchange diagnostic for the TFTR is comprised of two analyzer systems which contain a total of twenty independent mass/energy analyzers and one diagnostic neutral beam tentatively rated at 80 keV, 15 A. The associated vacuum systems were analyzed using the Vacuum System Transient Simulator (VSTS) computer program which models the transient transport of multi-gas species through complex networks of ducts, valves, traps, vacuum pumps, and other related vacuum system components. In addition to providing improved design performance at reduced cost, the analysis yields estimates for the exchange of tritium from the torus to the diagnostic components and of the diagnostic working gases to the torus

An Advanced Diagnostic Display for Core Protection Calculator System

Energy Technology Data Exchange (ETDEWEB)

Kim, Ji-Hyeon; Jeong, See-Chae; Sohn, Se-Do [Korea Power Engineering Company, Daejeon (Korea, Republic of)

2008-10-15

The main purpose of a Nuclear Power Plant Instrumentation and Control (I and C) Display System is to provide operator's interface for I and C systems. The CPCS display(Shin-Kori 1 and 2) provides operators with 1) plant monitoring values of field input and algorithm variables that reflect the reactor core conditions, 2) operation values that operators can change and 3) CPCS status. It will be an optimal case if operators can understand the plant (including CPCS itself) condition intuitively with the displayed values but it is not easy in CPCS. For example, if the CPCS Channel Trouble light is lit, operators need some amount of time to investigate what caused the trouble light because there are more than hundred causes that can generate the channel trouble. If a Display supports diagnostic information that shows what cause the displayed alarms, it will greatly help operators in easy understanding the CPCS status. To provide these diagnostic information, this paper suggests an active self-explanatory display mechanism. This self-explanatory diagnostic display mechanism utilizes an ontology in XML that describes parent child, sibling relationships of display variables, through which in-depth, in-breadth diagnostic tracking is possible. This paper consists of two parts. First, the key features of CPCS Flat Panel Display System (FPDS) are described. Second, the features of active self explanatory diagnostic display are discussed.

An Advanced Diagnostic Display for Core Protection Calculator System

International Nuclear Information System (INIS)

Kim, Ji-Hyeon; Jeong, See-Chae; Sohn, Se-Do

2008-01-01

The main purpose of a Nuclear Power Plant Instrumentation and Control (I and C) Display System is to provide operator's interface for I and C systems. The CPCS display(Shin-Kori 1 and 2) provides operators with 1) plant monitoring values of field input and algorithm variables that reflect the reactor core conditions, 2) operation values that operators can change and 3) CPCS status. It will be an optimal case if operators can understand the plant (including CPCS itself) condition intuitively with the displayed values but it is not easy in CPCS. For example, if the CPCS Channel Trouble light is lit, operators need some amount of time to investigate what caused the trouble light because there are more than hundred causes that can generate the channel trouble. If a Display supports diagnostic information that shows what cause the displayed alarms, it will greatly help operators in easy understanding the CPCS status. To provide these diagnostic information, this paper suggests an active self-explanatory display mechanism. This self-explanatory diagnostic display mechanism utilizes an ontology in XML that describes parent child, sibling relationships of display variables, through which in-depth, in-breadth diagnostic tracking is possible. This paper consists of two parts. First, the key features of CPCS Flat Panel Display System (FPDS) are described. Second, the features of active self explanatory diagnostic display are discussed

Intelligent failure-proof control system for structural vibration

Energy Technology Data Exchange (ETDEWEB)

Yoshida, Kazuo [Keio Univ., Yokohama (Japan). Faculty of Science and Technology; Oba, Takahiro [Keio Univ., Tokyo (Japan)

2000-11-01

With progress of technology in recent years, gigantism and complication such as high-rise buildings, nuclear reactors and so on have brought about new problems. Particularly, the safety and the reliability for damages in abnormal situations have become more important. Intelligent control systems which can judge whether the situation is normal or abnormal at real time and cope with these situations suitably are demanded. In this study, Cubic Neural Network (CNN) is adopted, which consists of the controllers possessing cubically some levels of information abstracting. In addition to the usual quantitative control, the qualitative control is used for the abnormal situations. And, by selecting a suitable controller, CNN can cope with the abnormal situation. In order to confirm the effectiveness of this system, the structural vibration control problems with sensory failure and elasto-plastic response are dealt with. As a result of simulations, it was demonstrated that CNN can cope with unexpected abnormal situations which are not considered in learning. (author)

Intelligent failure-proof control system for structural vibration

International Nuclear Information System (INIS)

Yoshida, Kazuo

2000-01-01

With progress of technology in recent years, gigantism and complication such as high-rise buildings, nuclear reactors and so on have brought about new problems. Particularly, the safety and the reliability for damages in abnormal situations have become more important. Intelligent control systems which can judge whether the situation is normal or abnormal at real time and cope with these situations suitably are demanded. In this study, Cubic Neural Network (CNN) is adopted, which consists of the controllers possessing cubically some levels of information abstracting. In addition to the usual quantitative control, the qualitative control is used for the abnormal situations. And, by selecting a suitable controller, CNN can cope with the abnormal situation. In order to confirm the effectiveness of this system, the structural vibration control problems with sensory failure and elasto-plastic response are dealt with. As a result of simulations, it was demonstrated that CNN can cope with unexpected abnormal situations which are not considered in learning. (author)

Target diagnostic control system implementation for the National Ignition Facility (invited)

Energy Technology Data Exchange (ETDEWEB)

Shelton, R. T.; Kamperschroer, J. H.; Lagin, L. J.; Nelson, J. R.; O' Brien, D. W. [Lawrence Livermore National Laboratory, Livermore, California 94550 (United States)

2010-10-15

The extreme physics of targets shocked by NIF's 192-beam laser is observed by a diverse suite of diagnostics. Many diagnostics are being developed by collaborators at other sites, but ad hoc controls could lead to unreliable and costly operations. A diagnostic control system (DCS) framework for both hardware and software facilitates development and eases integration. Each complex diagnostic typically uses an ensemble of electronic instruments attached to sensors, digitizers, cameras, and other devices. In the DCS architecture each instrument is interfaced to a low-cost WINDOWS XP processor and JAVA application. Each instrument is aggregated with others as needed in the supervisory system to form an integrated diagnostic. The JAVA framework provides data management, control services, and operator graphical user interface generation. DCS instruments are reusable by replication with reconfiguration for specific diagnostics in extensible markup language. Advantages include minimal application code, easy testing, and high reliability. Collaborators save costs by assembling diagnostics with existing DCS instruments. This talk discusses target diagnostic instrumentation used on NIF and presents the DCS architecture and framework.

Target diagnostic control system implementation for the National Ignition Facility (invited)

International Nuclear Information System (INIS)

Shelton, R. T.; Kamperschroer, J. H.; Lagin, L. J.; Nelson, J. R.; O'Brien, D. W.

2010-01-01

The extreme physics of targets shocked by NIF's 192-beam laser is observed by a diverse suite of diagnostics. Many diagnostics are being developed by collaborators at other sites, but ad hoc controls could lead to unreliable and costly operations. A diagnostic control system (DCS) framework for both hardware and software facilitates development and eases integration. Each complex diagnostic typically uses an ensemble of electronic instruments attached to sensors, digitizers, cameras, and other devices. In the DCS architecture each instrument is interfaced to a low-cost WINDOWS XP processor and JAVA application. Each instrument is aggregated with others as needed in the supervisory system to form an integrated diagnostic. The JAVA framework provides data management, control services, and operator graphical user interface generation. DCS instruments are reusable by replication with reconfiguration for specific diagnostics in extensible markup language. Advantages include minimal application code, easy testing, and high reliability. Collaborators save costs by assembling diagnostics with existing DCS instruments. This talk discusses target diagnostic instrumentation used on NIF and presents the DCS architecture and framework.

Comparative guide to emerging diagnostic tools for large commercial HVAC systems; TOPICAL

International Nuclear Information System (INIS)

Friedman, Hannah; Piette, Mary Ann

2001-01-01

This guide compares emerging diagnostic software tools that aid detection and diagnosis of operational problems for large HVAC systems. We have evaluated six tools for use with energy management control system (EMCS) or other monitoring data. The diagnostic tools summarize relevant performance metrics, display plots for manual analysis, and perform automated diagnostic procedures. Our comparative analysis presents nine summary tables with supporting explanatory text and includes sample diagnostic screens for each tool

Nike Facility Diagnostics and Data Acquisition System

Science.gov (United States)

Chan, Yung; Aglitskiy, Yefim; Karasik, Max; Kehne, David; Obenschain, Steve; Oh, Jaechul; Serlin, Victor; Weaver, Jim

2013-10-01

The Nike laser-target facility is a 56-beam krypton fluoride system that can deliver 2 to 3 kJ of laser energy at 248 nm onto targets inside a two meter diameter vacuum chamber. Nike is used to study physics and technology issues related to laser direct-drive ICF fusion, including hydrodynamic and laser-plasma instabilities, material behavior at extreme pressures, and optical and x-ray diagnostics for laser-heated targets. A suite of laser and target diagnostics are fielded on the Nike facility, including high-speed, high-resolution x-ray and visible imaging cameras, spectrometers and photo-detectors. A centrally-controlled, distributed computerized data acquisition system provides robust data management and near real-time analysis feedback capability during target shots. Work supported by DOE/NNSA.

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Science.gov (United States)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Electron beam diagnostic system using computed tomography and an annular sensor

Science.gov (United States)

Elmer, John W.; Teruya, Alan T.

2014-07-29

A system for analyzing an electron beam including a circular electron beam diagnostic sensor adapted to receive the electron beam, the circular electron beam diagnostic sensor having a central axis; an annular sensor structure operatively connected to the circular electron beam diagnostic sensor, wherein the sensor structure receives the electron beam; a system for sweeping the electron beam radially outward from the central axis of the circular electron beam diagnostic sensor to the annular sensor structure wherein the electron beam is intercepted by the annular sensor structure; and a device for measuring the electron beam that is intercepted by the annular sensor structure.

A Massively Parallel Face Recognition System

Directory of Open Access Journals (Sweden)

Lahdenoja Olli

2007-01-01

Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

A Massively Parallel Face Recognition System

Directory of Open Access Journals (Sweden)

Ari Paasio

2006-12-01

Full Text Available We present methods for processing the LBPs (local binary patterns with a massively parallel hardware, especially with CNN-UM (cellular nonlinear network-universal machine. In particular, we present a framework for implementing a massively parallel face recognition system, including a dedicated highly accurate algorithm suitable for various types of platforms (e.g., CNN-UM and digital FPGA. We study in detail a dedicated mixed-mode implementation of the algorithm and estimate its implementation cost in the view of its performance and accuracy restrictions.

A modern diagnostic approach for automobile systems condition monitoring

Science.gov (United States)

Selig, M.; Shi, Z.; Ball, A.; Schmidt, K.

2012-05-01

An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

A modern diagnostic approach for automobile systems condition monitoring

International Nuclear Information System (INIS)

Selig, M; Ball, A; Shi, Z; Schmidt, K

2012-01-01

An important topic in automotive research and development is the area of active and passive safety systems. In general, it is grouped in active safety systems to prevent accidents and passive systems to reduce the impact of a crash. An example for an active system is ABS while a seat belt tensioner represents the group of passive systems. Current developments in the automotive industry try to link active with passive system components to enable a complete event sequence, beginning with the warning of the driver about a critical situation till the automatic emergency call after an accident. The cross-linking has an impact on the current diagnostic approach, which is described in this paper. Therefore, this contribution introduces a new diagnostic approach for automotive mechatronic systems. The concept is based on monitoring the messages which are exchanged via the automotive communication systems, e.g. the CAN bus. According to the authors' assumption, the messages on the bus are changing between faultless and faulty vehicle condition. The transmitted messages of the sensors and control units are different depending on the condition of the car. First experiments are carried and in addition, the hardware design of a suitable diagnostic interface is presented. Finally, first results will be presented and discussed.

A beam diagnostic system for ELSA

International Nuclear Information System (INIS)

Schillo, M.; Althoff, K.H.; Drachenfels, W.; Goetz, T.; Husmann, D.; Neckenig, M.; Picard, M.; Schittko, F.J.; Schauerte, W.; Wenzel, J.

1991-01-01

A beam diagnostic system, which is based on capacitive beam-position monitors combined with fast electronics, has been developed for the Bonn ELectron Stretcher Accelerator ELSA. The position signal of each monitor is digitized at an adjustable sampling rate (max.: 10 MHz) and the most recent 8192 position and intensity values are buffered. This allows a wide range of different beam diagnostic measurements. The main purpose is the closed-orbit correction, which can be carried out on various time scales. To optimize the duty factor of the extracted beam, the system can also be used as a fast relative intensity monitor resolving the intensity distribution of the bunches or of the injected beam. It is designed to support betatron tune and phase measurements with very high accuracy, offering the choice to select any of the beam position monitors. This enables the measuring of many optical parameters. Furthermore any pair of suitable monitors can be used for experimental particle tracking or phase space measurements

Evaluation Of The Diagnostic Performance Of A Multimedia Medical Communications System.

Science.gov (United States)

Robertson, John G.; Coristine, Marjorie; Goldberg, Morris; Beeton, Carolyn; Belanger, Garry; Tombaugh, Jo W.; Hickey, Nancy M.; Millward, Steven F.; Davis, Michael; Whittingham, David

1989-05-01

The central concern of radiologists when evaluating Picture Archiving Communication System (PACS) is the diagnostic performance of digital images compared to the original analog versions of the same images. Considerable work has been done comparing the ROC curves of various types of digital systems to the corresponding analog systems for the detection of specific phantoms or diseases. Although the studies may notify the radiologists that for a specific lesion a digital system may perform as well as the analog system, it tells the radiologists very little about the impact on diagnostic performance of a digital system in the general practice of radiology. We describe in this paper an alternative method for evaluating the diagnostic performance of a digital system and a preliminary experiment we conducted to test the methodology.

BIOTECHNICAL SYSTEM FOR INTEGRATED OLFACTOMETRY DIAGNOSTICS

Directory of Open Access Journals (Sweden)

Yana Nosova

2017-09-01

Full Text Available The subject matter of the article is the process of olfactometric research of a human olfactory function. The object of the study is a biotechnical system which includes a method for increasing the objectivity of olfactometric diagnostics. The goal is to develop a biotechnical system for complex olfactometry, which enables increasing the objectivity of olfactometric studies and connecting breathing parameters with olfactory function by placing an odorant carrier in the airway of the rhinomanometer, as well as by using procedures for determining the energy characteristics of respiration. The methods used are: methods of digital signal processing, the theory of biotechnical systems. The following results are obtained. A structural diagram of a biotechnical system for olfactometric diagnostics of a human olfactory analyser was developed. As a result of the analysis of the cyclogram of nasal breathing, it is found that by changing the frequency and nature of breathing upon reaching the sensitivity threshold, it is possible to objectify the method of assessing respiratory and olfactory disorders according to the energy criterion of pneumatic power when inhaling appropriate odorivectors, and also to study the olfactory and respiratory function with the capability of estimating respiratory cycles in a dynamic mode. The studies were carried out on the basis of typical inspiration cycles: with a quiet breathing in the normal conditions, in the forceful breathing mode with a stiff nasal valve, with a nasal valve with natural functional mobility which restricts the flow of air, and also a stepped inspiration – a short “sipping” of air, which can be characterized as a kind of “sniffing”. Conclusions. Computer olfactometry is one of the most promising methods for diagnosing olfactory disorders of respiratory genesis. The developed biotechnical system is based on the use of a fundamentally new design, combining a rhinomanometer and an olfactometric

Development of an equipment diagnostic system that evaluates sensor drift

International Nuclear Information System (INIS)

Kanada, Masaki; Arita, Setsuo; Tada, Nobuo; Yokota, Katsuo

2011-01-01

The importance of condition monitoring technology for equipment has increased with the introduction of condition-based maintenance in nuclear power plants. We are developing a diagnostic system using process signals for plant equipment, such as pumps and motors. It is important to enable the diagnostic system to distinguish sensor drift and equipment failure. We have developed a sensor drift diagnostic method that combines some highly correlative sensor signals by using the MT (Mahalanobis-Taguchi) method. Furthermore, we have developed an equipment failure diagnostic method that measures the Mahalanobis distance from the normal state of equipment by the MT method. These methods can respectively detect sensor drift and equipment failure, but there are the following problems. In the sensor drift diagnosis, there is a possibility of misjudging the sensor drift when the equipment failure occurs and the process signal changes because the behavior of the process signal is the same as that of the sensor drift. Oppositely, in the equipment failure diagnosis, there is a possibility of misjudging the equipment failure when the sensor drift occurs because the sensor drift influences the change of process signal. To solve these problems, we propose a diagnostic method combining the sensor drift diagnosis and the equipment failure diagnosis by the MT method. Firstly, the sensor drift values are estimated by the sensor drift diagnosis, and the sensor drift is removed from the process signal. It is necessary to judge the validity of the estimated sensor drift values before removing the sensor drift from the process signal. We developed a method for judging the validity of the estimated sensor drift values by using the drift distribution based on the sensor calibration data. And then, the equipment failure is diagnosed by using the process signals after removal of the sensor drifts. To verify the developed diagnostic system, several sets of simulation data based on abnormal cases

Prototyping low-cost and flexible vehicle diagnostic systems

Directory of Open Access Journals (Sweden)

Marisol GARCÍA-VALLS

2016-12-01

Full Text Available Diagnostic systems are software and hardware-based equipment that interoperate with an external monitored system. Traditionally, they have been expensive equipment running test algorithms to monitor physical properties of, e.g., vehicles, or civil infrastructure equipment, among others. As computer hardware is increasingly powerful (whereas its cost and size is decreasing and communication software becomes easier to program and more run-time efficient, new scenarios are enabled that yield to lower cost monitoring solutions. This paper presents a low cost approach towards the development of a diagnostic systems relying on a modular component-based approach and running on a resource limited embedded computer. Results on a prototype implementation are shown that validate the presented design, its flexibility, performance, and communication latency.

Diagnostic technology and an expert system for photovoltaic systems using the learning method

Energy Technology Data Exchange (ETDEWEB)

Yagi, Yasuhiro; Kishi, Hitoshi; Hagihara, Ryuzou; Tanaka, Toshiya; Kozuma, Shinichi; Ishida, Takeo; Waki, Masahiro; Tanaka, Makoto; Kiyama, Seiichi [SANYO Electric Co. Ltd., New Materials Research Center, Moriguchi City, Osaka (Japan)

2003-02-01

Diagnostic technology for photovoltaic (PV) systems was developed, using the learning method to take each site's conditions into account. This technology employs diagnostic criteria databases to analyze data acquired from the PV systems. These criteria are updated monthly for each site using analyzed data. To check the shadows on the PV modules and pyranometer, the sophisticated verification method was also applied to this technology. After the diagnosis, a basket method provides maintenance advice for the PV systems. Based on the results of precise diagnoses, this expert system offers quick and proper maintenance advice within a few minutes. This technology is highly useful, because it greatly simplifies the servicing and maintenance of PV systems. (Author)

Real time PV manufacturing diagnostic system

Energy Technology Data Exchange (ETDEWEB)

Kochergin, Vladimir [MicroXact Inc., Blacksburg, VA (United States); Crawford, Michael A. [MicroXact Inc., Blacksburg, VA (United States)

2015-09-01

The main obstacle Photovoltaic (PV) industry is facing at present is the higher cost of PV energy compared to that of fossil energy. While solar cell efficiencies continue to make incremental gains these improvements are so far insufficient to drive PV costs down to match that of fossil energy. Improved in-line diagnostics however, has the potential to significantly increase the productivity and reduce cost by improving the yield of the process. On this Phase I/Phase II SBIR project MicroXact developed and demonstrated at CIGS pilot manufacturing line a high-throughput in-line PV manufacturing diagnostic system, which was verified to provide fast and accurate data on the spatial uniformity of thickness, an composition of the thin films comprising the solar cell as the solar cell is processed reel-to-reel. In Phase II project MicroXact developed a stand-alone system prototype and demonstrated the following technical characteristics: 1) ability of real time defect/composition inconsistency detection over 60cm wide web at web speeds up to 3m/minute; 2) Better than 1mm spatial resolution on 60cm wide web; 3) an average better than 20nm spectral resolution resulting in more than sufficient sensitivity to composition imperfections (copper-rich and copper-poor regions were detected). The system was verified to be high vacuum compatible. Phase II results completely validated both technical and economic feasibility of the proposed concept. MicroXact’s solution is an enabling technique for in-line PV manufacturing diagnostics to increase the productivity of PV manufacturing lines and reduce the cost of solar energy, thus reducing the US dependency on foreign oil while simultaneously reducing emission of greenhouse gasses.

Climate Model Diagnostic Analyzer Web Service System

Science.gov (United States)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

2015-12-01

Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the

New method of leak detecting in diagnostic of gas pipeline system

International Nuclear Information System (INIS)

Kalinowski, K.; Dabrowski, A.; Sobkiewicz, D.; Oracz, H.

2007-01-01

This report describes new directions in gas transmission pipelines diagnostics as well as new methods and equipment used to detect leaks. It was also shown that efficient and functional diagnostics system is the necessary condition to keep the exploitation of transmission systems safe. (author)

Filterscope diagnostic system on EAST tokamak

International Nuclear Information System (INIS)

Xu, Z.; Wu, Z.W.; Gao, W.; Zhang, L.; Huang, J.; Chen, Y.J.; Wu, C.R.; Zhang, P.F.

2015-01-01

Filterscope diagnostic system, which is designed for monitoring the line emission in fusion plasma has been widely used on fusion devices such as DIII-D, NSTX, CDX-U, KSTAR etc. On EAST (Experimental Advanced Superconducting Tokamak), a filterscope diagnostic system has been mounted to observe the line emission and visible bremsstrahlung emission in plasma from discharge campaign of 2014. It plays a crucial role in studying Edge Localized Modes (ELM) and H-mode, thanks to its high temporal resolution (0.005ms) and good spatial resolution (∼2cm). Furthermore, multi-channel signals at up to 200kHz sampling rates can be digitized simultaneously. The wavelength covers He II (468.5nm), Li I (670.8nm), Li II (548.3nm), C III (465.0nm), O II (441.5nm), Mo I (386.4nm), W I (400.9nm) and visible bremsstrahlung radiation at 538nm besides Dα (656.1nm) and Dγ (433.9nm) with the corresponding wavelength filters. The new developed filterscope system was operating during the EAST 2014 fall experimental campaign and several types ELMs has been observed. (author)

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

Science.gov (United States)

Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

2017-11-21

Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

Science.gov (United States)

Esses, Steven J; Lu, Xiaoguang; Zhao, Tiejun; Shanbhogue, Krishna; Dane, Bari; Bruno, Mary; Chandarana, Hersh

2018-03-01

To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 -weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728. © 2017 International Society for Magnetic Resonance in Medicine.

Diagnostics

DEFF Research Database (Denmark)

Donné, A.J.H.; Costley, A.E.; Barnsley, R.

2007-01-01

of the measurements—time and spatial resolutions, etc—will in some cases be more stringent. Many of the measurements will be used in the real time control of the plasma driving a requirement for very high reliability in the systems (diagnostics) that provide the measurements. The implementation of diagnostic systems...... on ITER is a substantial challenge. Because of the harsh environment (high levels of neutron and gamma fluxes, neutron heating, particle bombardment) diagnostic system selection and design has to cope with a range of phenomena not previously encountered in diagnostic design. Extensive design and R......&D is needed to prepare the systems. In some cases the environmental difficulties are so severe that new diagnostic techniques are required. The starting point in the development of diagnostics for ITER is to define the measurement requirements and develop their justification. It is necessary to include all...

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

Science.gov (United States)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Directory of Open Access Journals (Sweden)

Shigang Zhang

2015-10-01

Full Text Available Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics.

Optimal Sequential Diagnostic Strategy Generation Considering Test Placement Cost for Multimode Systems

Science.gov (United States)

Zhang, Shigang; Song, Lijun; Zhang, Wei; Hu, Zheng; Yang, Yongmin

2015-01-01

Sequential fault diagnosis is an approach that realizes fault isolation by executing the optimal test step by step. The strategy used, i.e., the sequential diagnostic strategy, has great influence on diagnostic accuracy and cost. Optimal sequential diagnostic strategy generation is an important step in the process of diagnosis system construction, which has been studied extensively in the literature. However, previous algorithms either are designed for single mode systems or do not consider test placement cost. They are not suitable to solve the sequential diagnostic strategy generation problem considering test placement cost for multimode systems. Therefore, this problem is studied in this paper. A formulation is presented. Two algorithms are proposed, one of which is realized by system transformation and the other is newly designed. Extensive simulations are carried out to test the effectiveness of the algorithms. A real-world system is also presented. All the results show that both of them have the ability to solve the diagnostic strategy generation problem, and they have different characteristics. PMID:26457709

Explanation of diagnostic criteria for radiation-induced nervous system disease

International Nuclear Information System (INIS)

Xing Zhiwei; Jiang Enhai

2012-01-01

National occupational health standard-Diagnostic Criteria for Radiation-Induced Nervous System Disease has been issued and implemented by the Ministry of health. This standard contained three independent criteria of the brain, spinal cord and peripheral nerve injury. These three kinds of disease often go together in clinic,therefore,the three diagnostic criteria were merged into radioactive nervous system disease diagnostic criteria for entirety and maneuverability of the standard. This standard was formulated based on collection of the clinical practice experience, extensive research of relevant literature and foreign relevant publications. It is mainly applied to diagnosis and treatment of occupational radiation-induced nervous system diseases, and to nervous system diseases caused by medical radiation exposure as well. In order to properly implement this standard, also to correctly deal with radioactive nervous system injury, the main contents of this standard including dose threshold, clinical manifestation, indexing standard and treatment principle were interpreted in this article. (authors)

Multi-probe ionization chamber system for nuclear-generated plasma diagnostics

International Nuclear Information System (INIS)

Choi, W.Y.; Ellis, W.H.

1990-01-01

This paper reports on the pulsed ionization chamber (PIC) plasma diagnostic system used in studies of nuclear seeded plasma kinetics upgraded to increase the capabilities and extend the range of plasma parameter measurements to higher densities and temperatures. The PIC plasma diagnostic chamber has been provided with additional measurement features in the form of conductivity and Langmuir probes, while the overall experimental system has been fully automated, with computerized control, measurement, data acquisition and analysis by means of IEEE-488 (GPIB) bus control and data transfer protocols using a Macintosh series microcomputer. The design and use of a simple TTL switching system enables remote switching among the various GPIB instruments comprising the multi-probe plasma diagnostic system using software, without the need for a microprocessor. The new system will be used to extend the present study of nuclear generated plasma in He, Ar, Xe, fissionable UF 6 and other fluorine containing gases

Diagnostic information management system for the evaluation of medical images

Energy Technology Data Exchange (ETDEWEB)

Higa, Toshiaki; Torizuka, Kanji; Minato, Kotaro; Komori, Masaru; Hirakawa, Akina

1985-04-01

A practical, small and low-cost diagnostic information management system has been developed for a comparative study of various medical imaging procedures, including ordinary radiography, X-ray computed tomography, emission computed tomography, and so forth. The purpose of the system is to effectively manage the original image data files and diagnostic descriptions during the various imaging procedures. A diagnostic description of each imaging procedure for each patient is made on a hand-sort punched-card with line-drawings and ordinary medical terminology and then coded and computerized using Index for Roentgen Diagnoses (American College of Radiology). A database management software (DB Master) on a personal computer (Apple II) is used for searching for patients' records on hand-sort punched-cards and finally original medical images. Discussed are realistic use of medical images and an effective form of diagnostic descriptions.

Diagnostic information management system for the evaluation of medical images

International Nuclear Information System (INIS)

Higa, Toshiaki; Torizuka, Kanji; Minato, Kotaro; Komori, Masaru; Hirakawa, Akina.

1985-01-01

A practical, small and low-cost diagnostic information management system has been developed for a comparative study of various medical imaging procedures, including ordinary radiography, X-ray computed tomography, emission computed tomography, and so forth. The purpose of the system is to effectively manage the original image data files and diagnostic descriptions during the various imaging procedures. A diagnostic description of each imaging procedure for each patient is made on a hand-sort punched-card with line-drawings and ordinary medical terminology and then coded and computerized using Index for Roentgen Diagnoses (American College of Radiology). A database management software (DB Master) on a personal computer (Apple II) is used for searching for patients' records on hand-sort punched-cards and finally original medical images. Discussed are realistic use of medical images and an effective form of diagnostic descriptions. (author)

PC based diagnostic system for nitrogen production unit of HWP

International Nuclear Information System (INIS)

Lamba, D.S.; Rao, V.C.; Krishnan, S.; Kamaraj, T.; Krishnaswamy, C.

1992-01-01

The plant diagnostic system monitors the input data from local processing unit and tries to diagnose the cause of the failure. The system is a rule based application program that can perform tasks itself using fault tree model which displays the logical relationships between critical events and their possible ways occurrence, i.e. hardware failure, process faults and human error etc. Unit 37 Nitrogen Plant is taken as a prototype model for trying the plant diagnostics system. (author). 3 refs., 2 figs

Fast infectious diseases diagnostics based on microfluidic biochip system

Directory of Open Access Journals (Sweden)

Qin Huang

2017-03-01

Full Text Available Molecular diagnostics is one of the most important tools currently in use for clinical pathogen detection due to its high sensitivity, specificity, and low consume of sample and reagent is keyword to low cost molecular diagnostics. In this paper, a sensitive DNA isothermal amplification method for fast clinical infectious diseases diagnostics at aM concentrations of DNA was developed using a polycarbonate (PC microfluidic chip. A portable confocal optical fluorescence detector was specifically developed for the microfluidic chip that was capable of highly sensitive real-time detection of amplified products for sequence-specific molecular identification near the optical diffraction limit with low background. The molecular diagnostics of Listeria monocytogenes with nucleic acid extracted from stool samples was performed at a minimum DNA template concentration of 3.65aM, and a detection limit of less than five copies of genomic DNA. Contrast to the general polymerase chain reaction (PCR at eppendorf (EP tube, the detection time in our developed method was reduced from 1.5h to 45min for multi-target parallel detection, the consume of sample and reagent was dropped from 25μL to 1.45μL. This novel microfluidic chip system and method can be used to develop a micro total analysis system as a clinically relevant pathogen molecular diagnostics method via the amplification of targets, with potential applications in biotechnology, medicine, and clinical molecular diagnostics.

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

Science.gov (United States)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

DIAGNOSTIC OF CNC LATHE WITH QC 20 BALLBAR SYSTEM

Directory of Open Access Journals (Sweden)

Jerzy Józwik

2015-11-01

Full Text Available This paper presents the evaluation of the influence of the feedmotion speed on the value of selected geometric errors of CNC lathe CTX 310 eco by DMG, indentified by QC 20 Ballbar system. Diagnostically evaluated were: the deviation of the axis squareness, reversal spike, and backlash. These errors determine the forming of the dimensional and shape accuracy of a machine tool. The article discusses the process of the CNC diagnostic test, the diagnostic evaluation and formulates guidelines on further CNC operation. The results of measurements were presented in tables and diagrams.

Chinese character recognition based on Gabor feature extraction and CNN

Science.gov (United States)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Autonomous power expert fault diagnostic system for Space Station Freedom electrical power system testbed

Science.gov (United States)

Truong, Long V.; Walters, Jerry L.; Roth, Mary Ellen; Quinn, Todd M.; Krawczonek, Walter M.

1990-01-01

The goal of the Autonomous Power System (APS) program is to develop and apply intelligent problem solving and control to the Space Station Freedom Electrical Power System (SSF/EPS) testbed being developed and demonstrated at NASA Lewis Research Center. The objectives of the program are to establish artificial intelligence technology paths, to craft knowledge-based tools with advanced human-operator interfaces for power systems, and to interface and integrate knowledge-based systems with conventional controllers. The Autonomous Power EXpert (APEX) portion of the APS program will integrate a knowledge-based fault diagnostic system and a power resource planner-scheduler. Then APEX will interface on-line with the SSF/EPS testbed and its Power Management Controller (PMC). The key tasks include establishing knowledge bases for system diagnostics, fault detection and isolation analysis, on-line information accessing through PMC, enhanced data management, and multiple-level, object-oriented operator displays. The first prototype of the diagnostic expert system for fault detection and isolation has been developed. The knowledge bases and the rule-based model that were developed for the Power Distribution Control Unit subsystem of the SSF/EPS testbed are described. A corresponding troubleshooting technique is also described.

Design considerations for on-line vibration diagnostic systems

International Nuclear Information System (INIS)

Branagan, L.A.; Schjeibel, J.R.

1989-01-01

The decisions made in the design of a data system for on-line vibration diagnostic system in power plants define how well the system will meet its intended goals. Direct use of the data for performing troubleshooting or developing operating correlations requires an understanding of the subtle impact of the design decisions incorporated in the data system. A data system includes data acquisition, data storage, and data retrieval. Data acquisition includes the selection of sensors, of vibration measurement modes, and of the time stamping format, and the arrangement of data collection cycles. Data storage requires the evaluation of data compression options and of data segregation. Data retrieval design requires an understanding of the data storage and acquisition techniques. Each of these options and design decisions involves compromises, many of which are discussed in this paper. Actual and synthetic data are presented to illustrate these points. The authors' experience with multiple data collection cycles, with frequent monitoring, and with storage by exception suggests that these techniques can be developed into an effective diagnostic system

Lithium beam diagnostic system on the COMPASS tokamak

Energy Technology Data Exchange (ETDEWEB)

Anda, G.; Bencze, A. [Wigner – RCP, HAS, Budapest (Hungary); Berta, M., E-mail: bertam@sze.hu [Institute of Plasma Physics AS CR, Prague (Czech Republic); Széchenyi István University, Győr (Hungary); Dunai, D. [Wigner – RCP, HAS, Budapest (Hungary); Hacek, P. [Institute of Plasma Physics AS CR, Prague (Czech Republic); Faculty of Mathematics and Physics, Charles University in Prague, Prague (Czech Republic); Krbec, J. [Institute of Plasma Physics AS CR, Prague (Czech Republic); Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague (Czech Republic); Réfy, D.; Krizsanóczi, T.; Bató, S.; Ilkei, T.; Kiss, I.G.; Veres, G.; Zoletnik, S. [Wigner – RCP, HAS, Budapest (Hungary)

2016-10-15

Highlights: • Li-beam diagnostic system on the COMPASS tokamak is an improved and compact system to allow testing of Atomic Beam Probe. • The possibility to measure background corrected density profiles on the few microseconds time scale. • First Li-beam diagnostic system with recirculating neutralizer. • The system includes the redesigned ion source with longer lifetime. - Abstract: An improved lithium beam based beam emission spectroscopy system – installed on COMPASS tokamak – is described. The beam energy enhanced up to 120 keV for Atomic Beam Probe measurement. The size of the ion source is doubled, using a newly developed thermionic heater instead of the conventionally used heating (tungsten or molybdenum) filament. The neutralizer is also improved. It produces the same sodium vapor in a cell but minimize the loss condensing the vapor on a cold surface which is led back (in fluid state) into the sodium oven. This way we call it recirculating neutralizer. The observation system consists of a CCD camera and an avalanche photodiode array.

Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

Science.gov (United States)

Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

2017-12-01

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

The development of the intelligent diagnostic expert system for high power dye-laser MOPA system

International Nuclear Information System (INIS)

Liu Lianhua; Yang Wenxi; Zhang Xiaowei; Dan Yongjun

2014-01-01

A intelligent diagnostic expert system was required to simulate the expert thinking process of solving problem in experiment and to real-time judge the running state of the experiment system. The intelligent diagnostic expert system for dye-laser MOPA system was build with the modular design of separated knowledge base and inference engine, the RETE algorithm rules match, the asynchronous operation, and multithreading technology. The experiment result indicated that the system could real-time analysis and diagnose the running state of dye-laser MOPA system with advantages of high diagnosis efficiency, good instantaneity and strong expansibility. (authors)

Role of theoretical dynamics in vibration diagnostics of pipe systems

International Nuclear Information System (INIS)

Rejent, B.

1992-01-01

The importance of vibration diagnostics of pipe systems and the relevance of theoretical dynamics are shown using examples. The problems are discussed of vibration diagnostics of the primary circuit of a nuclear power plant with viscous seismic dampers installed. (M.D.) 7 figs., 5 refs

GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

Energy Technology Data Exchange (ETDEWEB)

Patel, Kiran, E-mail: kkpatel@ipr.res.in; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

2016-11-15

The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

International Nuclear Information System (INIS)

Patel, Kiran; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

2016-01-01

The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

Diagnostics of the vibrations of complex rotor systems

Science.gov (United States)

Yugraytis, I. Y.; Ragulskis, K. M.; Ionushas, R. A.; Karuzhene, I. P.

1973-01-01

The parameters of the imbalance of a complex rotor system, having n parallel rotors and having six degrees of freedom, can be determined from the parameters of the vibrations of two appropriate degrees of freedom. This considerably simplifies diagnostics of the vibrations of complex rotor systems.

Improving diagnostic accuracy using agent-based distributed data mining system.

Science.gov (United States)

Sridhar, S

2013-09-01

The use of data mining techniques to improve the diagnostic system accuracy is investigated in this paper. The data mining algorithms aim to discover patterns and extract useful knowledge from facts recorded in databases. Generally, the expert systems are constructed for automating diagnostic procedures. The learning component uses the data mining algorithms to extract the expert system rules from the database automatically. Learning algorithms can assist the clinicians in extracting knowledge automatically. As the number and variety of data sources is dramatically increasing, another way to acquire knowledge from databases is to apply various data mining algorithms that extract knowledge from data. As data sets are inherently distributed, the distributed system uses agents to transport the trained classifiers and uses meta learning to combine the knowledge. Commonsense reasoning is also used in association with distributed data mining to obtain better results. Combining human expert knowledge and data mining knowledge improves the performance of the diagnostic system. This work suggests a framework of combining the human knowledge and knowledge gained by better data mining algorithms on a renal and gallstone data set.

The Diagnostic System of A – 604 Automatic Transmission

Directory of Open Access Journals (Sweden)

Czaban Jaroslaw

2014-09-01

Full Text Available Automatic gearbox gains increasing popularity in Europe. Little interest in diagnosis of such type of transmission in Poland results from the fact of small share in the whole market of operated cars, so there is a lack of availability of special diagnostic devices. These factors cause issues of expensive repairs, often involving a replacement of subassembly to new or aftermarket one. To a small extent some prophylactic diagnostic tests are conducted, which can eliminate future gearbox system failures. In the paper, the proposition of diagnostic system of popular A - 604 gearbox was presented. The authors are seeking for the possibility of using such type of devices to functional elaboration of gearboxes after renovation. The built system pursues the drive of the researched object, connected with simulated load, where special controller, replacing the original one, is responsible for controlling gearbox operation. This way is used to evaluate the mechanic and hydraulic parts' state. Analysis of signal runs, registered during measurements lets conclude about operation correctness, where as comparison with stock data verifies the technical state of an automatic gearbox.

Symptom based diagnostic system using artificial neural networks

International Nuclear Information System (INIS)

Santosh; Vinod, Gopika; Saraf, R.K.

2003-01-01

Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

The Diagnostic Value of Brain Scanning in the Diseases of the Central Nervous System

Energy Technology Data Exchange (ETDEWEB)

Kim, Kwang Won; Lee, Myung Chul; Koh, Chang Soon; Lee, Mun Ho; Chang, Kee Hyun; Han, Man Chung; Choi, Kil Su; Son, Hyo Chung; Cho, Byung Kyu [Seoul National University College of Medicine, Seoul (Korea, Republic of)

1974-03-15

The purpose of this study is to evaluate the diagnostic value of the brain scanning and compare the diagnostic accuracy between the scan and carotid angiography. 109 cases which are proved by specific method to each disease, are analyzed to evaluate the diagnostic value of the brain scanning. The 70 cases among the proven 109 case are performed both the scanning and the arteriography and analyzed to compare the accuracy between the scanning and the arteriography. The results are as follows; 1) The diagnostic accuracy of the brain scanning in the diseases of the central nervous system is 64.2%. 2) The diagnostic accuracy of the brain scanning in the brain tumor is 88%, especially brain abscess, glioma, glioblastoma multiforme, meningioma and metastic tumor show high positive rate. 3) The diagnostic accuracy in the disease of the brain vessels is 54%. The comparison of the diagnostic value between the scanning and the arteriography is as follows;1) The diagnostic value in all diseases of the central nervous system is nearly equal. 2) The diagnostic accuracy in the intracranial tumor is slightly higher in the brain scanning (90. 9%) than in the arteriography (81.8%). 3) The diagnostic accuracy in the disease of the brain vessel is higher in the arteriography (77.3%) than in the scanning (54.5%). 5) The diagnostic value when combining the scanning and the arteriography, is 83% in the all central nervous system-lesions, 97% in the cranial tumor and 81.8% in the disease of the central nervous system-vessel. The brain scanning is simple and safe procedure, and moreover has excellent diagnostic value in the diagnosis of the central nervous system lesion.

The Diagnostic Value of Brain Scanning in the Diseases of the Central Nervous System

International Nuclear Information System (INIS)

Kim, Kwang Won; Lee, Myung Chul; Koh, Chang Soon; Lee, Mun Ho; Chang, Kee Hyun; Han, Man Chung; Choi, Kil Su; Son, Hyo Chung; Cho, Byung Kyu

1974-01-01

The purpose of this study is to evaluate the diagnostic value of the brain scanning and compare the diagnostic accuracy between the scan and carotid angiography. 109 cases which are proved by specific method to each disease, are analyzed to evaluate the diagnostic value of the brain scanning. The 70 cases among the proven 109 case are performed both the scanning and the arteriography and analyzed to compare the accuracy between the scanning and the arteriography. The results are as follows; 1) The diagnostic accuracy of the brain scanning in the diseases of the central nervous system is 64.2%. 2) The diagnostic accuracy of the brain scanning in the brain tumor is 88%, especially brain abscess, glioma, glioblastoma multiforme, meningioma and metastic tumor show high positive rate. 3) The diagnostic accuracy in the disease of the brain vessels is 54%. The comparison of the diagnostic value between the scanning and the arteriography is as follows;1) The diagnostic value in all diseases of the central nervous system is nearly equal. 2) The diagnostic accuracy in the intracranial tumor is slightly higher in the brain scanning (90. 9%) than in the arteriography (81.8%). 3) The diagnostic accuracy in the disease of the brain vessel is higher in the arteriography (77.3%) than in the scanning (54.5%). 5) The diagnostic value when combining the scanning and the arteriography, is 83% in the all central nervous system-lesions, 97% in the cranial tumor and 81.8% in the disease of the central nervous system-vessel. The brain scanning is simple and safe procedure, and moreover has excellent diagnostic value in the diagnosis of the central nervous system lesion.

Overview of the data acquisition and control system for plasma diagnostics on MFTF-B

International Nuclear Information System (INIS)

Wyman, R.H.; Deadrick, F.J.; Lau, N.H.; Nelson, B.C.; Preckshot, G.G.; Throop, A.L.

1983-01-01

For MFTF-B, the plasma diagnostics system is expected to grow from a collection of 12 types of diagnostic instruments, initially producing about 1 Megabyte of data per shot, to an expanded set of 22 diagnostics producing about 8 Megabytes of data per shot. To control these diagnostics and acquire and process the data, a system design has been developed which uses an architecture similar to the supervisory/local-control computer system which is used to control other MFTF-B subsystems. This paper presents an overview of the hardware and software that will control and acquire data from the plasma diagnostics system. Data flow paths from the instruments, through processing, and into final archived storage will be described. A discussion of anticipated data rates, including anticipated software overhead at various points of the system, is included, along with the identification of possible bottlenecks. A methodology for processing of the data is described, along with the approach to handle the planned growth in the diagnostic system. Motivations are presented for various design choices which have been made

Preliminary consideration of CFETR ITER-like case diagnostic system.

Science.gov (United States)

Li, G S; Yang, Y; Wang, Y M; Ming, T F; Han, X; Liu, S C; Wang, E H; Liu, Y K; Yang, W J; Li, G Q; Hu, Q S; Gao, X

2016-11-01

Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

Preliminary consideration of CFETR ITER-like case diagnostic system

International Nuclear Information System (INIS)

Li, G. S.; Liu, Y. K.; Gao, X.; Yang, Y.; Wang, Y. M.; Ming, T. F.; Han, X.; Liu, S. C.; Wang, E. H.; Yang, W. J.; Li, G. Q.; Hu, Q. S.

2016-01-01

Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

Diagnostic Risk Adjustment for Medicaid: The Disability Payment System

Science.gov (United States)

Kronick, Richard; Dreyfus, Tony; Lee, Lora; Zhou, Zhiyuan

1996-01-01

This article describes a system of diagnostic categories that Medicaid programs can use for adjusting capitation payments to health plans that enroll people with disability. Medicaid claims from Colorado, Michigan, Missouri, New York, and Ohio are analyzed to demonstrate that the greater predictability of costs among people with disabilities makes risk adjustment more feasible than for a general population and more critical to creating health systems for people with disability. The application of our diagnostic categories to State claims data is described, including estimated effects on subsequent-year costs of various diagnoses. The challenges of implementing adjustment by diagnosis are explored. PMID:10172665

Breast Cancer Diagnostic System Final Report CRADA No. TC02098.0

Energy Technology Data Exchange (ETDEWEB)

Rubenchik, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DaSilva, L. B. [BioTelligent, Inc., Livermore, CA (United States)

2017-09-06

This was a collaborative effort between Lawrence Livermore National Security, LLC (formerly The Regents of the University of California)/Lawrence Liver more National Laboratory (LLNL) and BioTelligent, Inc. together with a Russian Institution (BioFil, Ltd.), to develop a new system ( diagnostic device, operating procedures, algorithms and software) to accurately distinguish between benign and malignant breast tissue (Breast Cancer Diagnostic System, BCDS).

Integrated Fault Diagnostics of Networks and IT Systems

Data.gov (United States)

National Aeronautics and Space Administration — The lecture of the Stanford-IVHM lecture series will give an overview of the approaches in building diagnostic solutions for networks and complex systems. The...

Optical fibres for fusion plasma diagnostics systems

International Nuclear Information System (INIS)

Brichard, B.

2005-01-01

The condition to achieve and maintain the ignition of a thermonuclear fusion plasma ignition calls for the construction of a large scale fusion reactor, namely ITER. This reactor is designed to deliver an average fusion power of 500 MW. The burning of fusion plasma at such high power level will release a tremendous amount of energy in the form of particle fluxes and ionising radiation. This energy release, primarily absorbed by the plasma facing components, can significantly degrade the performances of the plasma diagnostic equipment surrounding the machine. To ensure a correct operation of the Tokamak we need to develop highly radiation-resistance devices. In plasma diagnostic systems, optical fibre is viewed as a convenient tool to transport light from the plasma edge to the diagnostic area. Radiation affects the optical performances of the fibre mainly by the occurrence of radiation-induced absorption and luminescence. Both effects degrade the light signal used for plasma diagnostic. SCK-CEN is currently assessing radiation-resistant glasses for optical fibres and is developing the associated qualification procedure. The main objectives of this study were to increase the lifetime of optical components in high radiation background and to develop a radiation resistance optical fibre capable to operate in the radiation background of ITER

Use of the target diagnostic control system in the National Ignition Facility

Energy Technology Data Exchange (ETDEWEB)

Shelton, R; Lagin, L; Nelson, J

2011-07-25

The extreme physics of targets shocked by NIF's 192-beam laser are observed by a diverse suite of diagnostics including optical backscatter, time-integrated, time resolved and gated X-ray sensors, laser velocity interferometry, and neutron time of flight. Diagnostics to diagnose fusion ignition implosion and neutron emissions have been developed. A Diagnostic Control System (DCS) for both hardware and software facilitates development and eases integration. Each complex diagnostic typically uses an ensemble of electronic instruments attached to sensors, digitizers, cameras, and other devices. In the DCS architecture each instrument is interfaced to a low-cost Window XP processor and Java application. Instruments are aggregated as needed in the supervisory system to form an integrated diagnostic. The Java framework provides data management, control services and operator GUI generation. During the past several years, over thirty-six diagnostics have been deployed using this architecture in support of the National Ignition Campaign (NIC). The DCS architecture facilitates the expected additions and upgrades to diagnostics as more experiments are performed. This paper presents the DCS architecture, framework and our experiences in using it during the NIC to operate, upgrade and maintain a large set of diagnostic instruments.

Use of the target diagnostic control system in the National Ignition Facility

International Nuclear Information System (INIS)

Shelton, R.; Lagin, L.; Nelson, J.

2011-01-01

The extreme physics of targets shocked by NIF's 192-beam laser are observed by a diverse suite of diagnostics including optical backscatter, time-integrated, time resolved and gated X-ray sensors, laser velocity interferometry, and neutron time of flight. Diagnostics to diagnose fusion ignition implosion and neutron emissions have been developed. A Diagnostic Control System (DCS) for both hardware and software facilitates development and eases integration. Each complex diagnostic typically uses an ensemble of electronic instruments attached to sensors, digitizers, cameras, and other devices. In the DCS architecture each instrument is interfaced to a low-cost Window XP processor and Java application. Instruments are aggregated as needed in the supervisory system to form an integrated diagnostic. The Java framework provides data management, control services and operator GUI generation. During the past several years, over thirty-six diagnostics have been deployed using this architecture in support of the National Ignition Campaign (NIC). The DCS architecture facilitates the expected additions and upgrades to diagnostics as more experiments are performed. This paper presents the DCS architecture, framework and our experiences in using it during the NIC to operate, upgrade and maintain a large set of diagnostic instruments.

MCNPX simulations of fast neutron diagnostics for accelerator-driven systems

Energy Technology Data Exchange (ETDEWEB)

Habob, Moinul

2005-12-15

In accelerator-driven systems, the neutron spectrum will extend all the way up to the incident beam energy, i.e., several hundred MeV or even up to GeV energies. The high neutron energy allows novel diagnostics with a set of measurement techniques that can be used in a sub-critical reactor environment. Such measurements are primarily connected to system safety and validation. This report shows that in-core fast-neutron diagnostics can be employed to monitor changes in the position of incidence of the primary proton beam onto the neutron production target. It has also been shown that fast neutrons can be used to detect temperature-dependent density changes in a liquid lead-bismuth target. Fast neutrons can escape the system via the beam pipe for the incident proton beam. Out-of-core monitoring of these so called back-streaming neutrons could potentially be used to monitor beam changes if the target has a suitable shape. Moreover, diagnostics of back-streaming neutrons might be used for validation of the system design.

MCNPX simulations of fast neutron diagnostics for accelerator-driven systems

International Nuclear Information System (INIS)

Habib, Moinul

2005-12-01

In accelerator-driven systems, the neutron spectrum will extend all the way up to the incident beam energy, i.e., several hundred MeV or even up to GeV energies. The high neutron energy allows novel diagnostics with a set of measurement techniques that can be used in a sub-critical reactor environment. Such measurements are primarily connected to system safety and validation. This report shows that in-core fast-neutron diagnostics can be employed to monitor changes in the position of incidence of the primary proton beam onto the neutron production target. It has also been shown that fast neutrons can be used to detect temperature-dependent density changes in a liquid lead-bismuth target. Fast neutrons can escape the system via the beam pipe for the incident proton beam. Out-of-core monitoring of these so called back-streaming neutrons could potentially be used to monitor beam changes if the target has a suitable shape. Moreover, diagnostics of back-streaming neutrons might be used for validation of the system design

VIBRO-DIAGNOSTIC SYSTEM ON BASIS OF PERSONAL COMPUTER

Directory of Open Access Journals (Sweden)

V. V. Bokut

2007-01-01

Full Text Available A system for vibration diagnostics based on a mobile computer and two-channel microprocessor measuring device has been developed. Usage of fast Hartley-Fourier transform allows to increase frequency resolution up to 25000 spectral lines that makes it possible to use the system for wide range of applications. 

Development of a computerized system for performance monitoring and diagnostics in nuclear power plants

International Nuclear Information System (INIS)

Chou, G.H.; Chao, H.J.

1995-01-01

An on-line computerized system for thermal performance monitoring and diagnostics has been developed at the Institute of Nuclear Energy Research (INER). It was the product of the ChinShan plant performance Monitoring, Analysis and Diagnostics Expert System (CS-MADES) project sponsored by Taiwan Power Company (TPC). The system can carry out turbine performance monitoring and analysis during normal operation, and yield diagnostic results of component degradation after finding out the missing generation problems. Three subsystems were generated to support the whole system framework. They are Test Data Processing Subsystem (TDPS), On-line Monitoring and Analysis Subsystem (OMAS), and Thermal Performance Diagnostics Expert System (TPDES). Some visible benefits have been gained so far through the prototype system installed at the Chinshan nuclear power station

EAST-AIA deployment under vacuum: Calibration of laser diagnostic system using computer vision

Energy Technology Data Exchange (ETDEWEB)

Yang, Yang, E-mail: yangyang@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd, Hefei, Anhui (China); Song, Yuntao; Cheng, Yong; Feng, Hansheng; Wu, Zhenwei; Li, Yingying; Sun, Yongjun; Zheng, Lei [Institute of Plasma Physics, Chinese Academy of Sciences, 350 Shushanhu Rd, Hefei, Anhui (China); Bruno, Vincent; Eric, Villedieu [CEA-IRFM, F-13108 Saint-Paul-Lez-Durance (France)

2016-11-15

Highlights: • The first deployment of the EAST articulated inspection arm robot under vacuum is presented. • A computer vision based approach to measure the laser spot displacement is proposed. • An experiment on the real EAST tokamak is performed to validate the proposed measure approach, and the results shows that the measurement accuracy satisfies the requirement. - Abstract: For the operation of EAST tokamak, it is crucial to ensure that all the diagnostic systems are in the good condition in order to reflect the plasma status properly. However, most of the diagnostic systems are mounted inside the tokamak vacuum vessel, which makes them extremely difficult to maintain under high vacuum condition during the tokamak operation. Thanks to a system called EAST articulated inspection arm robot (EAST-AIA), the examination of these in-vessel diagnostic systems can be performed by an embedded camera carried by the robot. In this paper, a computer vision algorithm has been developed to calibrate a laser diagnostic system with the help of a monocular camera at the robot end. In order to estimate the displacement of the laser diagnostic system with respect to the vacuum vessel, several visual markers were attached to the inner wall. This experiment was conducted both on the EAST vacuum vessel mock-up and the real EAST tokamak under vacuum condition. As a result, the accuracy of the displacement measurement was within 3 mm under the current camera resolution, which satisfied the laser diagnostic system calibration.

Integrated control and diagnostic system architectures for future installations

International Nuclear Information System (INIS)

Wood, R.; March-Leuba, J.

2000-01-01

Nuclear reactors of the 21st century will employ increasing levels of automation and fault tolerance to increase availability, reduce accident risk, and lower operating costs. Key developments in control algorithms, fault diagnostics, fault tolerance, and distributed communications are needed to implement the fully automated plant. It will be equally challenging to integrate developments in separate information and control fields into a cohesive system, which collectively achieves the overall goals of improved safety, reliability, maintainability, and cost-effectiveness. Under the Nuclear Energy Research Initiative (NERI), the US Department of Energy is sponsoring a project to address some of the technical issues involved in meeting the long-range goal of 21st century reactor control systems. This project involves researchers from Oak Ridge National Laboratory, the University of Tennessee, and North Carolina State University. The research tasks under this project focus on some of the first-level breakthroughs in control design, diagnostic techniques, and information system design that will provide a path to enable the design process to be automated in the future. This paper describes the conceptual development of an integrated nuclear plant control and information system architecture, which incorporates automated control system development that can be traced to a set of technical requirements. The expectation is that an integrated plant architecture with optimal control and efficient use of diagnostic information can reduce the potential for operational errors and minimize challenges to the plant safety systems

Development and testing of a diagnostic system for intelligen distributed control at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Ruhl, D.W.; Klevans, E.H.; Robinson, G.E.

1990-01-01

A diagnostic system is under development for demonstration of Intelligent Distributed Control at the Experimental Breeder Reactor (EBR--II). In the first phase of the project a diagnostic system is being developed for the EBR-II steam plant based on the DISYS expert systems approach. Current testing uses recorded plant data and data from simulated plant faults. The dynamical simulation of the EBR-II steam plant uses the Babcock and Wilcox (B ampersand W) Modular Modeling System (MMS). At EBR-II the diagnostic system operates in the UNIX workstation and receives live plant data from the plant Data Acquisition System (DAS). Future work will seek implementation of the steam plant diagnostic in a distributed manner using UNIX based computers and Bailey microprocessor-based control system. 10 refs., 6 figs

Diagnostic System of Drill Condition in Laminated Chipboard Drilling Process

Directory of Open Access Journals (Sweden)

Swiderski Bartosz

2017-01-01

Full Text Available The paper presents an on-line automatic system for recognition of the drill condition in a laminated chipboard drilling process. Two states of the drill are considered: the sharp enough (still able to drill holes acceptable for processing quality and worn out (excessive drill wear, not satisfactory from the quality point of view of the process. The automatic system requires defining the diagnostic features, which are used as the input attributes to the classifier. The features have been generated from 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. The statistical parameters defined on the basis of the auto regression model of these signals have been used as the diagnostic features. The sequential step-wise feature selection is applied for choosing the most discriminative set of features. The final step of recognition is done by support vector machine classifier working in leave one out mode. The results of numerical experiments have confirmed good quality of the proposed diagnostic system.

Prognostic Enhancements to Diagnostic Systems (PEDS) Applied to Shipboard Power Generation Systems

National Research Council Canada - National Science Library

Byington, Carl S; Roemer, Michael J; Watson, Matthew J

2004-01-01

.... The current paper describes a framework and development process that allows more plug n play integration of new diagnostic and prognostic technologies using evolving Open System Architecture (OSA) standards...

Diagnostic system for combine cycle power plant

International Nuclear Information System (INIS)

Shimizu, Yujiro; Nomura, Masumi; Tanaka, Satoshi; Ito, Ryoji; Kita, Yoshiyuki

2000-01-01

We developed the Diagnostic System for Combined Cycle Power Plant which enables inexperienced operators as well as experienced operators to cope with abnormal conditions of Combined Cycle Power Plant. The features of this system are the Estimate of Emergency Level for Operation and the Prediction of Subsequent Abnormality, adding to the Diagnosis of Cause and the Operation Guidance. Moreover in this system, Diagnosis of Cause was improved by using our original method and support screens can be displayed for educational means in normal condition as well. (Authors)

Diagnostic omission errors in acute paediatric practice: impact of a reminder system on decision-making

Directory of Open Access Journals (Sweden)

Fisher Helen

2006-11-01

Full Text Available Abstract Background Diagnostic error is a significant problem in specialities characterised by diagnostic uncertainty such as primary care, emergency medicine and paediatrics. Despite wide-spread availability, computerised aids have not been shown to significantly improve diagnostic decision-making in a real world environment, mainly due to the need for prolonged system consultation. In this study performed in the clinical environment, we used a Web-based diagnostic reminder system that provided rapid advice with free text data entry to examine its impact on clinicians' decisions in an acute paediatric setting during assessments characterised by diagnostic uncertainty. Methods Junior doctors working over a 5-month period at four paediatric ambulatory units consulted the Web-based diagnostic aid when they felt the need for diagnostic assistance. Subjects recorded their clinical decisions for patients (differential diagnosis, test-ordering and treatment before and after system consultation. An expert panel of four paediatric consultants independently suggested clinically significant decisions indicating an appropriate and 'safe' assessment. The primary outcome measure was change in the proportion of 'unsafe' workups by subjects during patient assessment. A more sensitive evaluation of impact was performed using specific validated quality scores. Adverse effects of consultation on decision-making, as well as the additional time spent on system use were examined. Results Subjects attempted to access the diagnostic aid on 595 occasions during the study period (8.6% of all medical assessments; subjects examined diagnostic advice only in 177 episodes (30%. Senior House Officers at hospitals with greater number of available computer workstations in the clinical area were most likely to consult the system, especially out of working hours. Diagnostic workups construed as 'unsafe' occurred in 47/104 cases (45.2%; this reduced to 32.7% following system

A proof-of-concept transient diagnostic expert system for BWRs [Boiling Water Reactors

International Nuclear Information System (INIS)

Yoshida, K.; Naser, J.A.

1988-05-01

A proof-of-concept transient diagnostic expert system has been developed to identify the cause and the type of an abnormal transient in a boiling water nuclear power plant. For this expert system development, the calculational results of the simulation code RETRAN were used as the knowledge source. The knowledge extracted from the RETRAN analyses was transformed into IF-THEN rules in the knowledge base for the expert system. An important feature of this expert system is the introduction of certainty factors to allow diagnosis even in the cases where data may be either missing or marked as invalid. To increase the capability of this diagnostic system to distinguish between similiar transients, backward chaining reasoning is used to support the forward chaining reasoning with certainty factors. Through this effort, it has been demonstrated that an expert system can be successfully used to create a transient diagnostic system. It has also successfully demonstrated that RETRAN can be used as the knowledge source for developing the knowledge base of the diagnostic system

Cognitive and system factors contributing to diagnostic errors in radiology.

Science.gov (United States)

Lee, Cindy S; Nagy, Paul G; Weaver, Sallie J; Newman-Toker, David E

2013-09-01

In this article, we describe some of the cognitive and system-based sources of detection and interpretation errors in diagnostic radiology and discuss potential approaches to help reduce misdiagnoses. Every radiologist worries about missing a diagnosis or giving a false-positive reading. The retrospective error rate among radiologic examinations is approximately 30%, with real-time errors in daily radiology practice averaging 3-5%. Nearly 75% of all medical malpractice claims against radiologists are related to diagnostic errors. As medical reimbursement trends downward, radiologists attempt to compensate by undertaking additional responsibilities to increase productivity. The increased workload, rising quality expectations, cognitive biases, and poor system factors all contribute to diagnostic errors in radiology. Diagnostic errors are underrecognized and underappreciated in radiology practice. This is due to the inability to obtain reliable national estimates of the impact, the difficulty in evaluating effectiveness of potential interventions, and the poor response to systemwide solutions. Most of our clinical work is executed through type 1 processes to minimize cost, anxiety, and delay; however, type 1 processes are also vulnerable to errors. Instead of trying to completely eliminate cognitive shortcuts that serve us well most of the time, becoming aware of common biases and using metacognitive strategies to mitigate the effects have the potential to create sustainable improvement in diagnostic errors.

Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments.

Science.gov (United States)

Kamatani, Takashi; Fukunaga, Koichi; Miyata, Kaede; Shirasaki, Yoshitaka; Tanaka, Junji; Baba, Rie; Matsusaka, Masako; Kamatani, Naoyuki; Moro, Kazuyo; Betsuyaku, Tomoko; Uemura, Sotaro

2017-12-04

For single-cell experiments, it is important to accurately count the number of viable cells in a nanoliter well. We used a deep learning-based convolutional neural network (CNN) on a large amount of digital data obtained as microscopic images. The training set consisted of 103 019 samples, each representing a microscopic grayscale image. After extensive training, the CNN was able to classify the samples into four categories, i.e., 0, 1, 2, and more than 2 cells per well, with an accuracy of 98.3% when compared to determination by two trained technicians. By analyzing the samples for which judgments were discordant, we found that the judgment by technicians was relatively correct although cell counting was often difficult by the images of discordant samples. Based on the results, the system was further enhanced by introducing a new algorithm in which the highest outputs from CNN were used, increasing the accuracy to higher than 99%. Our system was able to classify the data even from wells with a different shape. No other tested machine learning algorithm showed a performance higher than that of our system. The presented CNN system is expected to be useful for various single-cell experiments, and for high-throughput and high-content screening.

General review of diagnostic systems in EDF PWR units

International Nuclear Information System (INIS)

Chevalier, R.; Brasseur, S.; Ricard, B.

1998-01-01

Since the beginning of the French nuclear program, Electricite de France (EDF) has looked for ways to improve the availability and safety of its nuclear units. Therefore, monitoring systems on turbogenerators, reactor coolant pumps, primary circuits and core internal structures were designed by the Research and Development Division and implemented with technologies available during the 1970's. However, mainly because of difficulties for data interpretation by plant personnel, EDF subsequently decided to design and develop different tools to help plant operators to process a diagnosis: - a new generation of the Monitoring and Diagnostic System called PSAD, - expert systems for diagnosis on reactor coolant pumps (RCP) 'DIAPO' and turbogenerator units (TG) 'DIVA', - diagnostic guides written for most equipment pending the installation of new monitoring and diagnosis systems. The first version of PSAD, installed in five units, performs on-line monitoring of the turbogenerator shaft line, steam inlet valves, the reactor coolant pumps and the generator stator. The second version not yet implemented, will include Loose Part Detection (LPD) and Core Internal Structure Monitoring (CISM). The level of diagnosis achieved by PSAD depends on the component monitored. The TG and RCP monitoring functions of PSAD compute high level diagnosis descriptors such as natural frequencies and long term trends but do not elaborate a diagnosis automatically. However, a diagnostic assistance window is available on-line, whenever a warning message is displayed, whether for immediate or later action. The window presents a diagnostic approach whose purpose is to find the causes of the symptoms observed. This diagnosis approach is automated in the DIVA and DIAPO expert systems. Numerous potential faults can be identified for both systems thanks to a hierarchy of abnormal situations. The user interactively answers questions when information is needed to progress in the diagnosis. The resulting

Field-based systems and advanced diagnostics

International Nuclear Information System (INIS)

Eryurek, E.

1998-01-01

Detection and characterization of anomalies in an industrial plant provide improved plant availability and plant efficiency thus yielding increased economic efficiency. Traditionally, detection of process anomalies is done at a high-level control system through various signal validation methods. These signal validation techniques rely on data from transmitters, which measure related process variables. Correlating these signals and deducing anomalies often is a very time consuming and a difficult task. Delays in detecting these anomalies can be costly during plant operation. Conventional centralized approaches also suffer from their dependence on detailed mathematical models of the processes. Smart field devices have the advantage of providing the necessary information directly to the control system as anomalies develop during operation of the processes enabling operators to take necessary steps to either prevent an unnecessary shut down before the problem becomes serious or schedule maintenance on the problematic loop. Fisher-Rosemount's PlantWeb TM architecture addresses 'Enhanced Measurement, Advanced Diagnostics and Control in the Field'. PlantWeb TM builds open process management systems by networking intelligent field devices, scalable control and systems platforms, and integrated modular software. A description of PlantWeb TM and how it improves various process conditions and reduces operating cost of a plant as well as a high level description of 'Enhanced Measurement, Advanced Diagnostics and Control in the Field', will be provided in this paper. PlantWeb TM is the trademark for Fisher-Rosemount's new field-based architecture that uses emerging technologies to utilize the power of intelligent field devices and deliver critical process and equipment information to improve plant performance. (author)

Development of Vibration Diagnostic System in Research Reactors

International Nuclear Information System (INIS)

EL-Kafas, A. A.

1999-01-01

Early failure detection and diagnosis system are an important group with increasing interest with the operating support system. Already existing system to monitor integrity of primary system components are vibration and acoustic monitoring system (2,3). The development of vibration diagnostic system for MARIA reactor (30 MW)-the second research reactor in Poland -was made. The new system is applied for the Egypt research reactor (ETRR-1). This paper represents the result obtained during the operation of this activity that carried out at MARIA and ETRR-1 reactors

Control systems for ITER diagnostics, heating and current drive

Energy Technology Data Exchange (ETDEWEB)

Simrock, Stefan, E-mail: stefan.simrock@iter.org

2016-11-15

The ITER Diagnostic, Heating and Current Drive systems might appear, on the face of it, to have very different control requirements. There are approximately 45 diagnostic systems, including magnetic sensors for plasma position and shape determination, imaging systems in the IR and visible, Thompson scattering for electron temperature and density, neutron detectors and collective scattering for alpha particle density and energy distribution. The H&CD systems encompass Electron Cyclotron Heating, using 24 1MW, 170 GHz gyrotrons and 5 steerable launchers to deliver 20 MW to the plasma, Ion Cyclotron Heating, using 8 3MW, 40–55 MHz sources and two multi-element launchers to deliver 20 MW to the plasma, and 2 Negative Ion Neutral Beam Injectors, each of which can deliver up to 16.5 MW of 1 MeV beams to the plasma. Although there are substantial differences in the needs for protection, when handling multi-MW heating systems, and in data throughput for many diagnostics, the formal processes needed to translate system requirements into Instrumentation and Control are identical. Due to the distributed procurement of ITER sub-systems and the need to integrate as painlessly as possible to CODAC, the formal processes, together with a substantial degree of standardization, are even more than usually essential. Starting from the technical, safety and protection, integration and operation requirements, a loop of functional analysis and signal listing is used to generate the controller configuration and the conceptual architecture. These elements in their turn lead to the physical and software design. The paper will describe the formal processes of control system design and the methods used by the ITER project to achieve the standardization of systems engineering practices. These have been applied to several use-cases covering all operation relevant phases such as plasma operation, maintenance, testing and conditioning. There are a number of running contracts that are developing

A vibroacoustic diagnostic system as an element improving road transport safety.

Science.gov (United States)

Komorska, Iwona

2013-01-01

Mechanical defects of a vehicle driving system can be dangerous on the road. Diagnostic systems, which monitor operations of electric and electronic elements and devices of vehicles, are continuously developed and improved, while defects of mechanical systems are still not managed properly. This article proposes supplementing existing on-board diagnostics with a system of diagnosing selected defects to minimize their impact. It presents a method of diagnosing mechanical defects of the engine, gearbox and other elements of the driving system on the basis of a model of the vibration signal obtained adaptively. This method is suitable for engine valves, engine head gasket, main gearbox, joints, etc.

The Development of System for Management of Enterprise: Diagnostic and Anticipative Approaches

Directory of Open Access Journals (Sweden)

Pawlowski Grzegorz

2017-03-01

Full Text Available The article is aimed at formation of theoretical and methodical foundations for development of a system for management of enterprise in line with the diagnostic and anticipative approaches. It has been determined that the diagnostic approach is based on the process of identification, analysis and assessment of the enterprise’s status (subject to restrictions on access to information resources to address the problematic moments and weaknesses of enterprise and/or use chances of functioning modalities and strong positions of enterprise to ensure its development and forming a perspective. At the same time, it has been determined that the anticipative approach is directed to an early warning and response to financial crisis, signaling to directors of enterprises on hazards, risks, and additional chances to increase efficiency and effectiveness of financial-economic activity by means of continuous monitoring of changes, arising in the environment of functioning. Prospect for further research will be developing a diagnostic system, taking into consideration the diagnostics in the enterprise’s management system and the theoretical-methodological foundations for development of system for management of enterprise.

Diagnostic expert system in the PF LINAC

International Nuclear Information System (INIS)

Abe, Isamu; Nakahara, Kazuo; Kitamura, Masaharu.

1992-01-01

A prototype diagnostic expert system (ES) was developed for the Photon Factory 2.5-GeV electron/positron LINAC injector system. The ES has been on-lined with the conventional linac computer network for receiving real data. This project was undertaken in an attempt to reduce the linac operator's mental workload, diagnosis duties, and to explore Artificial Intelligence (AI) technologies. The outlook for ES and its problems, and what has been achieved are outlined in this presentation. (author)

Design and implementation of a Macintosh-CAMAC based system for neutral beam diagnostics

International Nuclear Information System (INIS)

Wight, J.; Hong, R.M.; Phillips, J.C.; Lee, R.L.; Colleraine, A.P.; Kim, J.

1989-12-01

An automated personal computer based CAMAC data acquisition system is being implemented on the DIII-D neutral beamlines for certain diagnostics. The waterflow calorimetry (WFC) diagnostic is the first system to be upgraded. It includes data acquisition by a Macintosh II computer containing a National Instruments IEEE-488 card, and running their LabView software. Macintosh to CAMAC communications are carried out through an IEEE-488 crate controller. The Doppler shift spectroscopy, residual gas analysis, and armor tile infrared image diagnostics will be modified in similar ways. To reduce the demand for Macintosh CPU time, the extensive serial high-way data activity is performed by means of a new Kinetic Systems 3982 List sequencing Crate Controller dedicated to these operations. A simple Local Area Network file server is used to store data from all diagnostics together, and in a format readable by a standard commercial database. This reduces the problem of redundant data storage and allows simpler inter-diagnostic analysis. 3 refs., 4 figs

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

Science.gov (United States)

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Dynamics model for real time diagnostics of Triga RC-1 system

International Nuclear Information System (INIS)

Gadomski, A.M.; Nanni, V.; Meo, G.

1988-01-01

This paper presents dynamics model of TRIGA RC-1 reactor system. The model is dedicated to the real-time early fault detection during a reactor operation in one week exploitation cycle. The algorithms are specially suited for real-time, long time and also accelerated simulations with assumed diagnostic oriented accuracy. The approximations, modular structure, numerical methods and validation are discussed. The elaborated model will be build in the TRIGA Supervisor System and TRIGA Diagnostic Simulator

Reactor accident diagnostic expert system: DISKET

International Nuclear Information System (INIS)

Yoshida, Kazuo; Yokobayashi, Masao

1989-11-01

A reactor accident diagnostic system DISKET has been developed to identify the cause and the type of an abnormal transient of a nuclear power plant. The system is based on the knowledge engineering and consists of an inference engine IERIAS and a knowledge base. The main features of DISKET are the following: Time-varying characteristics of transient can be treated and knowledge base can be divided into several knowledge units to handle a lot of rules effectively. This report has been provided for the convenience of DISKET's users and consists of three parts. The first part is the description of the whole system, the details of the knowledge base of DISKET are described in the second part, and how to use the DISKET system is explained in the third part. (author)

Efficient Probabilistic Diagnostics for Electrical Power Systems

Science.gov (United States)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

A diagnostic scoring system for myxedema coma.

Science.gov (United States)

Popoveniuc, Geanina; Chandra, Tanu; Sud, Anchal; Sharma, Meeta; Blackman, Marc R; Burman, Kenneth D; Mete, Mihriye; Desale, Sameer; Wartofsky, Leonard

2014-08-01

To develop diagnostic criteria for myxedema coma (MC), a decompensated state of extreme hypothyroidism with a high mortality rate if untreated, in order to facilitate its early recognition and treatment. The frequencies of characteristics associated with MC were assessed retrospectively in patients from our institutions in order to derive a semiquantitative diagnostic point scale that was further applied on selected patients whose data were retrieved from the literature. Logistic regression analysis was used to test the predictive power of the score. Receiver operating characteristic (ROC) curve analysis was performed to test the discriminative power of the score. Of the 21 patients examined, 7 were reclassified as not having MC (non-MC), and they were used as controls. The scoring system included a composite of alterations of thermoregulatory, central nervous, cardiovascular, gastrointestinal, and metabolic systems, and presence or absence of a precipitating event. All 14 of our MC patients had a score of ≥60, whereas 6 of 7 non-MC patients had scores of 25 to 50. A total of 16 of 22 MC patients whose data were retrieved from the literature had a score ≥60, and 6 of 22 of these patients scored between 45 and 55. The odds ratio per each score unit increase as a continuum was 1.09 (95% confidence interval [CI], 1.01 to 1.16; P = .019); a score of 60 identified coma, with an odds ratio of 1.22. The area under the ROC curve was 0.88 (95% CI, 0.65 to 1.00), and the score of 60 had 100% sensitivity and 85.71% specificity. A score ≥60 in the proposed scoring system is potentially diagnostic for MC, whereas scores between 45 and 59 could classify patients at risk for MC.

REXS : A financial risk diagnostic expert system

Directory of Open Access Journals (Sweden)

W. Richter

2012-01-01

Full Text Available

ENGLISH ABSTRACT: Artificial intelligence techniques are rapidly emerging as important contributors to more effective management. One of the greatest growth areas probably lies in the use of Expert System methodology for supporting managerial decision processes.
Existing Decision Support Systems often attempt to apply analytical techniques in combination with traditional data access and retrieval functions. One of the problems usually encountered while developing such decision support systems is the need to transform an unstructured problem environment into a structured analytical model. Using an expert system approach to strategic decision making in such unstructured problem environments may provide significant advantages.
The financial Risk diagnostic EXpert System (REXS concentrates on Financial Risk Analysis. Based on a Forecasting Model the system will, with the support of several expert system knowledge bases, attempt to evaluate the financial risk of a business and provide guidelines for improvement.

AFRIKAANSE OPSOMMING: Tegnieke gebaseer op Kunsmatige Intelligensie toon tans die belofte om belangrike bydraes te maak tot meerBestaande Besluitsteunstelsels poog dikwels om analitiese tegnieke en lradisionele datatoegang- en onttrekkingsfunksies te kombineer. Een van die probleme wat gewoonlik ondervind word gedurende die ontwikkeling van '0 besluitsteunstelsel bestaan uit die behoefte om 'n ongestruktueerde probleemomgewing te transformeer na 'n gestruktueerde analitiese model. 'n Ekspertstelselbenadering lot strategiese besluitneming in 'n ongeSlruktureerde probleemomgewing mag betekenisvolle voordele inhou.
Die "financial Risk diagnostic EXpert System (REXS" konsentreer op fmansiele risiko-analise. Uitgaande vanaf 'n Vooruitskattingsmode~ en deur gebruik te maak van verskeie ekspertstelselkennisbasisse, poog die stelsel om die fmansiele risiko van 'n onderneming te evalueer en riglyne vir moontlike verbetering

Chemistry monitoring and diagnostic system at NPP Jaslovske Bohunice

International Nuclear Information System (INIS)

Smiesko, Ivan; Figedy, Stefan

2012-09-01

This paper provides a description of water chemistry monitoring and diagnostic system installed at Slovak NPP Jaslovske Bohunice. System has complex architecture and covers laboratory data, chemistry and radiochemistry on-line monitoring data, process data acquisition and processing and diagnostics. Pre-filtered data from process computer and chemistry on-line monitors are recorded together with laboratory data in the ORACLE-based information system CHEMIS with many presentation and processing features. Brief information is given about the basic features of a newly developed diagnostic system for early detection and identification of anomalies incoming in the water chemistry regime of the primary and secondary circuit of VVER-440 type unit. This system, called SACHER (System of Analysis of Chemical Regime) has been installed within the major modernization project at the NPP Bohunice in the Slovak Republic. System SACHER has been developed fully in MATLAB environment. Diagnostic system works exclusively with available on-line data as an operation personnel support application allowing effective response to adverse chemistry events/trends. The availability of prompt information about the chemical conditions of the primary and secondary circuit is very important in order to prevent the undue corrosion and deposit build-up processes within the plant systems. The typical chemical information systems that exist and work at the NPPs give the user values of the measured quantities together with their time trends and other derived values. It is then the experienced user's role to recognize the situation the monitored process is in and make the subsequent decisions and take the measures. The SACHER system, based on the computational intelligence techniques, inserts the elements of intelligence into the overall chemical information system. It has the modular structure with the following most important modules: - normality module- its aim is to recognize that the process

Applications of FASTBUS to beam diagnostics and experiment data systems

International Nuclear Information System (INIS)

Machen, D.R.

1983-01-01

A five-year effort by the North American NIM Committee, in collaboration with the ESONE Committee of European Laboratories, has resulted in a specification for the FASTBUS modular high-speed data-acquisition system. The system is designed around an emitter-coupled logic (ECL) 32-bit data bus for asynchronous data transmission at 100 ns per transaction. Initial applications of FASTBUS will be in experiment data systems at accelerator facilities worldwide--beam diagnostic data systems on the accelerator side and particle-beam experiment data systems in the experimental area. As the specification (and the resulting hardware and software) matures, real-time machine-control applications will become possible. This paper discusses the near-term use of FASTBUS in accelerator beam-diagnostics instrumentation systems, where an extra increment in system throughput and front-end processing speed can produce a greater understanding of the physical phenomena under study. The arguments and conclusions may be equally well applied to other similar data-handling problems requiring high bandwidth in the data system

Diagnostics and reliability of pipeline systems

CERN Document Server

Timashev, Sviatoslav

2016-01-01

The book contains solutions to fundamental problems which arise due to the logic of development of specific branches of science, which are related to pipeline safety, but mainly are subordinate to the needs of pipeline transportation.          The book deploys important but not yet solved aspects of reliability and safety assurance of pipeline systems, which are vital aspects not only for the oil and gas industry and, in general, fuel and energy industries , but also to virtually all contemporary industries and technologies. The volume will be useful to specialists and experts in the field of diagnostics/ inspection, monitoring, reliability and safety of critical infrastructures. First and foremost, it will be useful to the decision making persons —operators of different types of pipelines, pipeline diagnostics/inspection vendors, and designers of in-line –inspection (ILI) tools, industrial and ecological safety specialists, as well as to researchers and graduate students.

Software for the diagnostic system of the secondary circuit of the Temelin nuclear power plant

International Nuclear Information System (INIS)

Drab, J.

1990-01-01

The secondary circuit of unit 1 of the Temelin nuclear power plant will be fitted with an automated diagnostic system, whose objects include the turbine and generator; feedwater pumps and their turbines; separator-reheater; condensers; low-pressure and high-pressure heaters; feedwater tank; and steam lines. The automated diagnostic system is divided into 5 subsystems, each containing a measuring unit controlled by a PC 286 computer. These computers are included in a LAN network with a PC 386 master computer. The software consists of 3 components, viz. ONSPEC for controlling the measuring unit, data evaluation and organization and for intercommunication within the LAN; diagnostic software for the diagnostic tests, of which a total of 23 are included; and communication software for transmitting the diagnostic test results to the unit control room and also for transmitting data from accurate sensors to the information computer system for technico-economic calculations. The whole system is open to future supplementing with additional software, diagnostic tests or diagnostic subsystems. (P.A.). 1 fig., 3 refs

Dynamics model for real time diagnostics of TRIGA RC-1 system

International Nuclear Information System (INIS)

Gadomski, A.M.; Nanni, V.; Meo, G.B.

1986-01-01

This paper presents dynamics model of TRIGA RC-1 reactor system. The model is dedicated to the real-time early fault detection during a reactor operation in one week exploitation cycle. The algorithms are specially suited for real-time, long time and also accelerated simulations with assumed diagnostic oriented accuracy. The approximations, modular structure, numerical methods and validation are discussed. The elaborated model will be build in the TRIGA Supervisory System and TRIGA Diagnostic Simulator. (author)

Development and Integration of a Data Acquisition System for SST-1 Phase-1 Plasma Diagnostics

International Nuclear Information System (INIS)

Srivastava, Amit K; Sharma, Manika; Mansuri, Imran; Sharma, Atish; Raval, Tushar; Pradhan, Subrata

2012-01-01

Long pulse (of the order of 1000 s or more) SST-1 tokamak experiments demand a data acquisition system that is capable of acquiring data from various diagnostics channels without losing useful data (and hence physics information) while avoiding unnecessary generation of a large volume data. SST-1 Phase-1 tokamak operation has been envisaged with data acquisition of several essential diagnostics channels. These channels demand data acquisition at a sampling rate ranging from 1 kilo samples per second (KSPS) to 1 mega samples per second (MSPS). Considering the technical characteristics and requirements of the diagnostics, a data acquisition system based on PXI and CAMAC has been developed for SST-1 plasma diagnostics. Both these data acquisition systems are scalable. Present data acquisition needs involving slow plasma diagnostics are catered by the PXI based data acquisition system. On the other hand, CAMAC data acquisition hardware meets all requirements of the SST-1 Phase-1 fast plasma diagnostics channels. A graphical user interface for both data acquisition systems (PXI and CAMAC) has been developed using LabVIEW application development software. The collected data on the local hard disk are directly streaming to the central server through a dedicated network for post-shot data analysis. This paper describes the development and integration of the data acquisition system for SST-1 Phase-1 plasma diagnostics. The integrated testing of the developed data acquisition system has been performed using SST-1 central control and diagnostics signal conditioning units. In the absence of plasma shots, the integrated testing of the data acquisition system for the initial diagnostics of SST-1 Phase-1 operation has been performed with simulated physical signals. The primary engineering objective of this integrated testing is to validate the performance of the developed data acquisition system under simulated conditions close to that of actual tokamak operation. The data

A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

Science.gov (United States)

Wu, G. Gordon

1995-01-01

Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

Validation Tools and Methods for Diagnostic Systems, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — The potential benefits of advanced algorithms for diagnostics and prognostics, inner-loop control, and other flight critical systems have been demonstrated in a...

Diagnostic and monitoring systems produced in Vuje, Okruzna 5, 918 64 Trnava, Slovak Republic

International Nuclear Information System (INIS)

Oksa, G.; Bahna, J.; Murin, V.; Kucharek, P.; Smutny, S.

1996-01-01

Based on the 20 years experience in on-line vibration diagnostics of mechanical components in the primary circuit of nuclear power plant PWR WWER-440, Vuje, Okruzna 5, 918 64 Trnava produces its own diagnostic and monitoring systems since 1990. The variety of diagnostic systems includes: loose part monitoring system (LPMS), monitoring system of main circulating pumps (MCPMS), vibration monitoring system (LVMS), leakage monitoring system (LMS). The emphasis in the hardware solution is put on the design modularity and versatility so that many subcomponents (circuit boards) are common or highly similar for all systems. Using exclusively digital data for processing enhances the reliability of the measurements and allows the easy data transportation from one computer to another (e.g., for more sophisticated analysis). Trends in the software development follow the similar path as for the hardware solution - namely, the modularity and versatility of software is the imperative goal. The modern operating systems also incorporate the ability of network communication, which is crucial for the integration of stand-alone diagnostic systems into nuclear power plants information system. So far a number of systems have been successfully installed: 6 LPMSs (Jaslovske Bohunice, Dukovany), 4 MCPMs (Jaslovske Bohunice) and 2 LVMSs (Jaslovske Bohunice), all systems operate in PWR WWER-440 environment. Another diagnostic systems are under construction: 2 LPMSs (Temelin, PWR WWER-1000), 2 MCMSs (Mochovce - PWR WWER-440) and 2 LMSs (Jaslovske Bohunice). (author). 1 fig

Diagnostic and monitoring systems produced in Vuje, Okruzna 5, 918 64 Trnava, Slovak Republic

Energy Technology Data Exchange (ETDEWEB)

Oksa, G; Bahna, J; Murin, V; Kucharek, P; Smutny, S [Vyskumny Ustav Jadrovych Elektrarni, Trnava (Slovakia)

1997-12-31

Based on the 20 years experience in on-line vibration diagnostics of mechanical components in the primary circuit of nuclear power plant PWR WWER-440, Vuje, Okruzna 5, 918 64 Trnava produces its own diagnostic and monitoring systems since 1990. The variety of diagnostic systems includes: loose part monitoring system (LPMS), monitoring system of main circulating pumps (MCPMS), vibration monitoring system (LVMS), leakage monitoring system (LMS). The emphasis in the hardware solution is put on the design modularity and versatility so that many subcomponents (circuit boards) are common or highly similar for all systems. Using exclusively digital data for processing enhances the reliability of the measurements and allows the easy data transportation from one computer to another (e.g., for more sophisticated analysis). Trends in the software development follow the similar path as for the hardware solution - namely, the modularity and versatility of software is the imperative goal. The modern operating systems also incorporate the ability of network communication, which is crucial for the integration of stand-alone diagnostic systems into nuclear power plants information system. So far a number of systems have been successfully installed: 6 LPMSs (Jaslovske Bohunice, Dukovany), 4 MCPMs (Jaslovske Bohunice) and 2 LVMSs (Jaslovske Bohunice), all systems operate in PWR WWER-440 environment. Another diagnostic systems are under construction: 2 LPMSs (Temelin, PWR WWER-1000), 2 MCMSs (Mochovce - PWR WWER-440) and 2 LMSs (Jaslovske Bohunice). (author). 1 fig.

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Feimo Li

2017-05-01

Full Text Available Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.

High frequency system project implementation plan. [Diagnostic recording system for Nevada Test Site

Energy Technology Data Exchange (ETDEWEB)

Moon, L. L.

1976-03-12

The High Frequency System is a new mobile, digital diagnostic recording system for use at the Nevada Test Site. Many different kinds of event data will be digitized in real-time by this system, and these data will be recorded and stored for later read-out and transmission to NADCEN. The hardware and software requirements of the High Frequency System are examined, and the parameters of the system are proposed.

NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

Directory of Open Access Journals (Sweden)

В. Єременко

2011-02-01

Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

An integrated real-time diagnostic concept using expert systems, qualitative reasoning and quantitative analysis

International Nuclear Information System (INIS)

Edwards, R.M.; Lee, K.Y.; Kumara, S.; Levine, S.H.

1989-01-01

An approach for an integrated real-time diagnostic system is being developed for inclusion as an integral part of a power plant automatic control system. In order to participate in control decisions and automatic closed loop operation, the diagnostic system must operate in real-time. Thus far, an expert system with real-time capabilities has been developed and installed on a subsystem at the Experimental Breeder Reactor (EBR-II) in Idaho, USA. Real-time simulation testing of advanced power plant concepts at the Pennsylvania State University has been developed and was used to support the expert system development and installation at EBR-II. Recently, the US National Science Foundation (NSF) and the US Department of Energy (DOE) have funded a Penn State research program to further enhance application of real-time diagnostic systems by pursuing implementation in a distributed power plant computer system including microprocessor based controllers. This paper summarizes past, current, planned, and possible future approaches to power plant diagnostic systems research at Penn State. 34 refs., 9 figs

Advanced monitoring, fault diagnostics, and maintenance of cryogenic systems

CERN Document Server

Girone, Mario; Pezzetti, Marco

In this Thesis, advanced methods and techniques of monitoring, fault diagnostics, and predictive maintenance for cryogenic processes and systems are described. In particular, in Chapter 1, mainstreams in research on measurement systems for cryogenic processes are reviewed with the aim of dening key current trends and possible future evolutions. Then, in Chapter 2, several innovative methods are proposed. A transducer based on a virtual ow meter is presented for monitoring helium distribution and consumption in cryogenic systems for particle accelerators [1]. Furthermore, a comprehensive metrological analysis of the proposed transducer for verifying the metrological performance and pointing out most critical uncertainty sources is described [2]. A model-based method for fault detection and early-stage isolation, able to work with few records of Frequency Response Function (FRF) on an unfaulty compressor, is then proposed [3]. To enrich the proposal, a distributed diagnostic procedure, based on a micro-genetic...

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Science.gov (United States)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Complete diagnostics of pyroactive structures for smart systems of optoelectronics

Science.gov (United States)

Bravina, Svetlana L.; Morozovsky, Nicholas V.

1998-04-01

The results of study of pyroelectric phenomena in ferroelectric materials for evidence of the possibility to embody the functions promising for creation of smart systems for optoelectronic applications are presented. Designing such systems requires the development of methods for non- destructive complete diagnostics preferably by developing the self-diagnostic ability inherent in materials with the features of smart/intelligent ones. The complex method of complete non-destructive qualification of pyroactive materials based on the method of dynamic photopyroelectric effect allows the determination of pyroelectric, piezoelectric, ferroelectric, dielectric and thermophysical characteristics. The measuring system which allows the study of these characteristics and also memory effects, switching effects, fatigue and degradation process, self-repair process and others is presented. Sample pyroactive system with increased intelligence, such as systems with built-in adaptive controllable domain structure promising for functional optics are developed and peculiarities of their characterization are discussed.

Study on fault diagnostic system using modularized knowledge; Mojuru gata chishiki wo mochiita ijo shindan system ni kansuru kenkyu

Energy Technology Data Exchange (ETDEWEB)

Shimada, Y.; Sayama, H.; Suzuki, K. [Okayama Univ. (Japan). Dept. of Industrial and Mechanical Engineering

1997-08-15

Recently, a fault diagnostic expert system was prosperously developed as an objective of chemical plants and nuclear power plants. In this paper, a fault diagnostic method using modularized knowledge was proposed, a fault diagnostic system was constructed for an experimental plant, and the effectiveness of this method was clarified by carrying out a fault diagnostic experiment. The characteristics of the proposed fault diagnostic system were as follows: The necessary knowledge for diagnosing faults was made into each process element. Based on this method, the revision and addition of a knowledge base could be carried out in each element, and the design change of a plant could be flexibly corresponded by only changing the related part of the process flow graph. The estimated results were stored into the working memory, not only faults of an element in which faults resulted could be estimated, but also the fault propagating path could be clarified. 8 refs., 6 figs., 3 tabs.

System control module diagnostic Expert Assistant

Science.gov (United States)

Flores, Luis M.; Hansen, Roger F.

1990-01-01

The Orbiter EXperiments (OEX) Program was established by NASA's Office of Aeronautics and Space Technology (OAST) to accomplish the precise data collection necessary to support a complete and accurate assessment of Space Transportation System (STS) Orbiter performance during all phases of a mission. During a mission, data generated by the various experiments are conveyed to the OEX System Control Module (SCM) which arranges for and monitors storage of the data on the OEX tape recorder. The SCM Diagnostic Expert Assistant (DEA) is an expert system which provides on demand advice to technicians performing repairs of a malfunctioning SCM. The DEA is a self-contained, data-driven knowledge-based system written in the 'C' Language Production System (CLIPS) for a portable micro-computer of the IBM PC/XT class. The DEA reasons about SCM hardware faults at multiple levels; the most detailed layer of encoded knowledge of the SCM is a representation of individual components and layouts of the custom-designed component boards.

Development of procedures to ensure quality and integrity in Tandem Mirror Experiment-Upgrade (TMX-U) diagnostics systems

International Nuclear Information System (INIS)

Coutts, G.W.; Coon, M.L.; Hinz, A.F.; Hornady, R.S.; Lang, D.D.; Lund, N.P.

1983-01-01

The diagnostic systems for Tandem Mirror Experiment-Upgrade (TMX-U) have grown from eleven initial systems to more than twenty systems. During operation, diagnostic system modifications are sometimes required to complete experimental objectives. Also, during operations new diagnostic systems are being developed and implemented. To ensure and maintain the quality and integrity of the data signals, a set of plans and systematic actions are being developed. This paper reviews the procedures set in place to maintain the integrity of existing data systems and ensure the performance objectives of new diagnostics being added

DIVA and DIAPO: two diagnostic knowledge based systems used for French nuclear power plants

International Nuclear Information System (INIS)

Porcheron, M.; Ricard, B.; Joussellin, A.

1997-01-01

In order to improve monitoring and diagnosis capabilities in nuclear power plants, Electricite de France (EDF) has designed an integrated monitoring and diagnosis assistance system: PSAD-Poste de Surveillance et d'Aide au Diagnostic. The development of such a sophisticated monitoring and data processing systems has emphasized the need for the addition of analysis and diagnosis assistance capabilities. Therefore, diagnostic knowledge based systems have been added to the functions monitored in PSAD: DIVA for turbine generators, and DIAPO for reactor coolant pumps. These systems were designed from a representation of the diagnostic reasoning process of experts and of the supporting knowledge. Diagnosis in both systems relies on an abduction reasoning process applied to component fault models and observations derived from their actual behavior, as provided by the monitoring functions. The basic theoretical elements of this diagnostic model are summarized in a first part. In a second part, DIVA and DIAPO specific elements are described

Axial diagnostic system of finished rods BWR type

International Nuclear Information System (INIS)

Rivero G, T.; Rojas C, E.L.

1996-01-01

This system is employed as a final non destructive diagnostic system to verify the adequate distribution of the different enrichment through the can of nuclear fuel. The system is framed of traction mechanisms, a personal computer, a counting card and another card for the pass motor control, the nuclear electronics and the control program. The performance is based on the gamma radiation counting of the natural decay of uranium 235, this radiation is processed by the nuclear instrumentation for delivering a pulse by each gamma detected. (Author)

Development of a majority vote decision module for a self-diagnostic monitoring system for an air-operated valve system

Energy Technology Data Exchange (ETDEWEB)

Kim, Woo Shin [Dept. of Information and Communication Engineering, Sejong University, Seoul (Korea, Republic of); Chai, Jang Bom [Dept. of Mechanical Engineering, Ajou University, Suwon (Korea, Republic of); Kim, In Taek [Dept. of Information and Communication Engineering, Myongji University, Yongin (Korea, Republic of)

2015-08-15

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable.

Development of a majority vote decision module for a self-diagnostic monitoring system for an air-operated valve system

International Nuclear Information System (INIS)

Kim, Woo Shin; Chai, Jang Bom; Kim, In Taek

2015-01-01

A self-diagnostic monitoring system is a system that has the ability to measure various physical quantities such as temperature, pressure, or acceleration from sensors scattered over a mechanical system such as a power plant, in order to monitor its various states, and to make a decision about its health status. We have developed a self-diagnostic monitoring system for an air-operated valve system to be used in a nuclear power plant. In this study, we have tried to improve the self-diagnostic monitoring system to increase its reliability. We have implemented three different machine learning algorithms, i.e., logistic regression, an artificial neural network, and a support vector machine. After each algorithm performs the decision process independently, the decision-making module collects these individual decisions and makes a final decision using a majority vote scheme. With this, we performed some simulations and presented some of its results. The contribution of this study is that, by employing more robust and stable algorithms, each of the algorithms performs the recognition task more accurately. Moreover, by integrating these results and employing the majority vote scheme, we can make a definite decision, which makes the self-diagnostic monitoring system more reliable

Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A.; Chasensky, T.M.

1996-01-01

Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use thermal hydraulic (T-H) signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance.To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. A full-scope operator training simulator representing the Commonwealth Edison Braidwood nuclear power plant is being used both as the source of development data and as the means to evaluate the advantages of the proposed diagnostic system. This is an ongoing multi-year project and this paper presents the results to date of the CRADA phase

Preliminary Calculations of Shutdown Dose Rate for the CTS Diagnostics System

DEFF Research Database (Denmark)

Klinkby, Esben Bryndt; Nonbøl, Erik; Lauritzen, Bent

2015-01-01

DTU and IST 2 are partners in the design of a collective Thomson Scattering (CTS) diagnostics for ITER through a contract with F4E. The CTS diagnostic utilizes probing radiation of ~60 GHz emitted into the plasma and, using a mirror, collects the scattered radiation by an array of receivers. Having...... on supplying input which affect the system design. Examples include: - Heatloads on plasma facing mirrors and preliminary stress and thermal analysis - Port plug cooling requirements and it's dependence on system design (in particular blanket cut-out) - Shutdown dose-rate calculations (relative analysis...

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

Science.gov (United States)

Kang, Jin Kyu; Hong, Hyung Gil; Park, Kang Ryoung

2017-07-08

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

Remote network control plasma diagnostic system for Tokamak T-10

International Nuclear Information System (INIS)

Troynov, V I; Zimin, A M; Krupin, V A; Notkin, G E; Nurgaliev, M R

2016-01-01

The parameters of molecular plasma in closed magnetic trap is studied in this paper. Using the system of molecular diagnostics, which was designed by the authors on the «Tokamak T-10» facility, the radiation of hydrogen isotopes at the plasma edge is investigated. The scheme of optical radiation registration within visible spectrum is described. For visualization, identification and processing of registered molecular spectra a new software is developed using MatLab environment. The software also includes electronic atlas of electronic-vibrational-rotational transitions for molecules of protium and deuterium. To register radiation from limiter cross-section a network control system is designed using the means of the Internet/Intranet. Remote control system diagram and methods are given. The examples of web-interfaces for working out equipment control scenarios and viewing of results are provided. After test run in Intranet, the remote diagnostic system will be accessible through Internet. (paper)

Handbook of technical diagnostics fundamentals and application to structures and systems

CERN Document Server

2013-01-01

This book presents concepts, methods and techniques to examine symptoms of faults and failures of structures, systems and components and to monitor functional performance and structural integrity. The book is organized in five parts. Part A introduces the scope and application of technical diagnostics and gives a comprehensive overview of the physics of failure. Part B presents all relevant methods and techniques for diagnostics and monitoring: from stress, strain, vibration analysis, nondestructive evaluation, thermography and industrial radiology to computed tomography and subsurface microstructural analysis. Part C cores the principles and concepts of technical failure analysis, illustrates case studies, and outlines machinery diagnostics with an emphasis on tribological systems. Part D describes the application of structural health monitoring and performance control to plants and the technical infrastructure, including buildings, bridges, pipelines, electric power stations, offshore wind structures, and r...

Improvement of malaria diagnostic system based on acridine orange staining.

Science.gov (United States)

Kimura, Masatsugu; Teramoto, Isao; Chan, Chim W; Idris, Zulkarnain Md; Kongere, James; Kagaya, Wataru; Kawamoto, Fumihiko; Asada, Ryoko; Isozumi, Rie; Kaneko, Akira

2018-02-07

Rapid diagnosis of malaria using acridine orange (AO) staining and a light microscope with a halogen lamp and interference filter was deployed in some malaria-endemic countries. However, it has not been widely adopted because: (1) the lamp was weak as an excitation light and the set-up did not work well under unstable power supply; and, (2) the staining of samples was frequently inconsistent. The halogen lamp was replaced by a low-cost, blue light-emitting diode (LED) lamp. Using a reformulated AO solution, the staining protocol was revised to make use of a concentration gradient instead of uniform staining. To evaluate this new AO diagnostic system, a pilot field study was conducted in the Lake Victoria basin in Kenya. Without staining failure, malaria infection status of about 100 samples was determined on-site per one microscopist per day, using the improved AO diagnostic system. The improved AO diagnosis had both higher overall sensitivity (46.1 vs 38.9%: p = 0.08) and specificity (99.0 vs 96.3%) than the Giemsa method (N = 1018), using PCR diagnosis as the standard. Consistent AO staining of thin blood films and rapid evaluation of malaria parasitaemia with the revised protocol produced superior results relative to the Giemsa method. This AO diagnostic system can be set up easily at low cost using an ordinary light microscope. It may supplement rapid diagnostic tests currently used in clinical settings in malaria-endemic countries, and may be considered as an inexpensive tool for case surveillance in malaria-eliminating countries.

USING THE INFORMATION OF ON-BOARD DIAGNOSTIC SYSTEMS IN DETERMINING THE TECHNICAL STATE OF THE LOCOMOTIVE

Directory of Open Access Journals (Sweden)

B. Ye. Bodnar

2008-12-01

Full Text Available The issues of increase of efficiency of information processing by оn-board systems of diagnostics of locomotives are considered. The examples of information processing by the on-board system of diagnostics of electric locomotives DE1 are presented. The suggestions on improvement of systematization and processing of information by on-board systems of diagnostics are given.

A diagnostic expert system for a boiling water reactor using a dynamic model

International Nuclear Information System (INIS)

Sonoda, Y.; Kanemoto, S.; Imaruoka, H.

1990-01-01

A diagnostic expert system for abnormal disturbances in a BWR (Boiling Water Reactor) plant has been developed. The peculiar feature of this system is a diagnostic method which combines artificial intelligence technique with numerical analysis technique. The system has three diagnostic functions, 1) identification of anomaly position (device or sensor), 2) identification of anomaly mode and 3) identification of anomaly cause. Function 1) is implemented as follows. First, a hypothesis about anomaly propagation paths is built up by qualitative reasoning, using knowledge of causal relations among observed signals. Next, the abnormal device or sensor is found by applying model reference method and fuzzy set theory to test the hypothesis, using knowledge of plant structure and function, heuristic strategy of diagnosis and module type dynamic simulator. This simulator is composed of basic transfer function modules. The simulation model for the testing region is built up automatically, according to the requirement from the diagnostic task. Function 2) means identification of dynamic characteristics for an anomaly. It is realized by tuning model parameters so as to reproduce the abnormal signal behavior using the non-linear programing method. Function 3) derives probable anomaly causes from heuristic rules between anomaly mode and cause. A basic plant dynamic model was built up and adjusted to dynamic characteristics for one BWR plant (1100MWe). In order to verify the diagnostic functions of this system, data for several abnormal events was compiled by modifying this model. The diagnostic functions were proved useful, through the simulated abnormal data

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

Directory of Open Access Journals (Sweden)

Dat Tien Nguyen

2017-10-01

Full Text Available Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD, is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor based on the observations of the researchers about the difference between real (live and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR camera-based finger-vein recognition system using convolutional neural network (CNN to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA for dimensionality reduction of feature space and support vector machine (SVM for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

Science.gov (United States)

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-10-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN

IGENPRO knowledge-based digital system for process transient diagnostics and management

International Nuclear Information System (INIS)

Morman, J.A.; Reifman, J.; Vitela, J.E.; Wei, T.Y.C.; Applequist, C.A.; Hippely, P.; Kuk, W.; Tsoukalas, L.H.

1998-01-01

Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R and D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R and D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management. (author)

IGENPRO knowledge-based digital system for process transient diagnostics and management

International Nuclear Information System (INIS)

Morman, J.A.; Reifman, J.; Wei, T.Y.C.

1997-01-01

Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R ampersand D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R ampersand D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management

Infrared laser scattering system for the plasma diagnostics

International Nuclear Information System (INIS)

Hiraki, Naoji; Kawasaki, Shoji; Muraoka, Katsunori

1975-01-01

As the results of the parametric studies of the double discharge TEA CO 2 laser, the required properties on the laser system for the scattering diagnostics of plasmas are shown to be realized with our CO 2 laser. The direction of the future improvements of the laser performance is also discussed. (auth.)

High speed imaging system for nuclear diagnostics

International Nuclear Information System (INIS)

Eyer, H.H.

1976-01-01

A high speed imaging system based on state-of-the-art photosensor arrays has been designed for use in nuclear diagnostics. The system is comprised of a front-end rapid-scan solid-state camera, a high speed digitizer, and a PCM line driver in a downhole package and a memory buffer system in a uphole trailer. The downhole camera takes a ''snapshot'' of a nuclear device created flux stream, digitizes the image and transmits it to the uphole memory system before being destroyed. The memory system performs two functions: it retains the data for local display and processing by a microprocessor, and it buffers the data for retransmission at slower rates to the LLL computational facility (NADS). The impetus for such a system as well as its operation are discussed. Also discussed are new systems under development which incorporate higher data rates and more resolution

High speed imaging system for nuclear diagnostics

International Nuclear Information System (INIS)

Eyer, H.H.

1976-01-01

A high speed imaging system based on state-of-the-art photosensor arrays has been designed for use in nuclear diagnostics. The system is comprised of a front-end rapid-scan solid-state camera, a high speed digitizer, and a PCM line driver in a downhole package and a memory buffer system in an uphole trailer. The downhole camera takes a ''snapshot'' of a nuclear device created flux stream, digitizes the image and transmits it to the uphole memory system before being destroyed. The memory system performs two functions: it retains the data for local display and processing by a microprocessor, and it buffers the data for retransmission at slower rates to the LLL computational facility (NADS). The impetus for such a system as well as its operation is discussed. Also discussed are new systems under development which incorporate higher data rates and more resolution

Constitution and application of reactor make-up system's fault diagnostic Bayesian networks

International Nuclear Information System (INIS)

Liang Jie; Cai Qi; Chu Zhuli; Wang Haiping

2013-01-01

A fault diagnostic Bayesian network of reactor make-up system was constituted. The system's structure characters, operation rules and experts' experience were combined and an initial net was built. As the fault date sets were learned with the particle swarm optimization based Bayesian network structure, the structure of diagnostic net was completed and used to inference case. The built net can analyze diagnostic probability of every node in the net and afford assistant decision to fault diagnosis. (authors)

Spontaneous Raman Scattering Diagnostics: Applications in Practical Combustion Systems. Chapter 5

Science.gov (United States)

Kojima, Jun; Viet-Nguyen, Quang; Lackner, Maximilian (Editor); Winter, Franz (Editor); Agarwal, Avinash (Editor)

2010-01-01

In this chapter, the recent advancements and practical aspects of laser SRS diagnostics have been reviewed wi til regards to applications in practical combustion systems. Clearly, SRS represents a theoretically and experimentally mature diagnostic technology that has become an essential tool for multiscalar measurements in turbulent combustion at elevated pressures. Today, time-, space-, spectrally, and even polarization-resolved S RS diagnostics is at hand, with aid from recent innovations in theoretical and technological developments on electro-optical or electromechanical devices. Whilst a linear increase in SRS signals can be expected in high-pressure systems (this is perhaps one of the most important advantages for using SRS in high-pressure systems), there are practical (often severe) restrictions associated with pressurized vessels, due mainly to the limited degree of optical access. This narrows ti,e available choice of diagnostics that can be employed at any given time. Point-wise SRS diagnostics provides the highest accuracy on the chemical species and temperature measurements, and will continue to remain a vital approach for the study in such harsh environments. The practical design considerations and hands-on set-up guide for SRS diagnostics provided in this chapter are rarely presented elsewhere. Although the second-harmonic Nd:YAG pulsed laser (532 nm), combined with pulse-stretching optics or the recently introduced White Cell-based laser, seems to be the most favored excitation source of choice by the research community, UV excitation will undoubtedly continue to be used on many occasions, and especially in sooting flames. Detection methods may be divided into ICCD-based nanosecond-gate detection or a rotary-chopper electromechanical shutter-based CCD array detection, and the levels of background flame emission in individual cases would determine this critical design choice. Here, a process of Raman signal calibration based on ti,e crosstalk matrix

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

Science.gov (United States)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

NPP Temelin automatic monitoring and diagnostic system of secondary side of the unit 1

International Nuclear Information System (INIS)

Heidenreich, S.; Pisl, M.; Drab, F.

1997-01-01

Two measuring and evaluating systems by SKODA PRAHA are described, designed for the Temelin nuclear power plant: a permanent secondary side diagnostic system for technology monitoring in the period of startup and operation; and the system of physical and power starting-up for monitoring during the first and repeated startup. The purposes of both systems are outlined. The scope of diagnostics covered and the functioning of the secondary side system is dealt with in some detail. (A.K.)

World of intelligence defense object detection-machine learning (artificial intelligence)

Science.gov (United States)

Gupta, Anitya; Kumar, Akhilesh; Bhushan, Vinayak

2018-04-01

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.

TV-acquired optical diagnostics systems on ATA

International Nuclear Information System (INIS)

Kalibjian, R.; Chong, Y.P.; Cornish, J.P.; Jackson, C.H.; Fessenden, T.J.

1984-06-01

The purpose of this paper is to report on optical system developments on the ATA and their applications to ATA beam characterization. Television (TV)-acquired optical diagnostics data provide spatial and temporal properties of the ATA beam that complements recorded information from other types of sensors, such as, beam-wall current monitors, x-ray probes, and rf probes. The ATA beam operates: (1) in the normal mode at 50-MeV, 10-kA at a 1-Hz rate; and (2) in the 1-KHz burst mode (for 10-pulses) at a 0.5 Hz rate. The beam has a 70-ns pulse width in vacuum propagation; however, beam-head erosion will occur in atmospheric propagation, thus limiting the pulse width to less than 50-ns. Various optical systems are used for ATA diagnostics. Optical-imaging provides a convenient measurement in a single pulse of the 2-dimensional profile of the beam intensity. It can also provide multiple 2-D framing in a single pulse. In some studies it may be desirable to study optical events with temporal resolution less than 100-ps with 1-dimensional streak cameras. Spatially integrated data from phototube cameras can also be used for background measurement applications as well as for single pixel monitoring. The optical line-of-sight (LOS) configurations have been made versatile to accommodate a large number of options for the various optical systems

Artificial intelligence system for technical diagnostics of photomasks

OpenAIRE

Kozin A. A.; Kozina Yu. Yu.

2012-01-01

The developed artificial intelligence system has a high level of noise immunity, so its inclusion in the hardware and software for technical diagnostics of photomasks will reduce the hardware requirements for its execution, and thereby reduce the cost of the complex. As a result it will allow to make a small-scale production profitable.

Mechanical considerations for MFTF-B plasma-diagnostic system

International Nuclear Information System (INIS)

Thomas, S.R. Jr.; Wells, C.W.

1981-01-01

The reconfiguration of MFTF to a tandem mirror machine with thermal barriers has caused a significant expansion in the physical scope of plasma diagnostics. From a mechanical perspective, it complicates the plasma access, system interfaces, growth and environmental considerations. Conceptual designs characterize the general scope of the design and fabrication which remains to be done

Evaluation of a vibration diagnostic system for the detection of spur gear pitting failures

Science.gov (United States)

Townsend, Dennis P.; Zakrajsek, James J.

1993-01-01

A vibration diagnostic system was used to detect spur gear surface pitting fatigue in a closed-loop spur gear fatigue test rig. The diagnostic system, comprising a personal computer with an analog-to-digital conversion board, a diagnostic system unit, and software, uses time-synchronous averaging of the vibration signal to produce a vibration image of each tooth on any gear in a transmission. Several parameters were analyzed including gear pair stress wave and raw baseband vibration, kurtosis, peak ratios, and others. The system provides limits for the various parameters and gives a warning when the limits are exceeded. Several spur gear tests were conducted with this system and vibration data analyzed at 5-min. intervals. The results presented herein show that the system is fairly effective at detecting spur gear tooth surface fatigue pitting failures.

Electrical probe diagnostic with fast data acquisition systems of the Novillo tokamak

International Nuclear Information System (INIS)

Lopez-Callejas, R.; Benitez-Read, J.S.; Longoria-Gandara, L.C.; Pacheco-Sotelo, J.O.; Valencia-Alvarado, R.; Tamayo, F.J.; Valdes, A.; Fernandez, M.C.; Serrano, F.

2001-01-01

This paper describes the electrical probe diagnostic system used to measure the relevant parameters of the plasma column generated in the Novillo tokamak, and an inexpensive and fast data acquisition system that consists of 16 independent channels, 8 bit resolution, sampling frequency of 500 kHz, and 8 kword memory per channel. A distributed, modular and transparent approach was used for designing the software-operated data acquisition, diagnostic, and capacitor banks triggering systems, satisfying the pulsed nature of the tokamak discharge. Through a personal computer, the experimental data are available to engineers and physicists in a centralized database

A Verification and Validation Tool for Diagnostic Systems, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Advanced diagnostic systems have the potential to improve safety, increase availability, and reduce maintenance costs in aerospace vehicle and a variety of other...

Development of the Diagnostic Expert System for Tea Processing

Science.gov (United States)

Yoshitomi, Hitoshi; Yamaguchi, Yuichi

A diagnostic expert system for tea processing which can presume the cause of the defect of the processed tea was developed to contribute to the improvement of tea processing. This system that consists of some programs can be used through the Internet. The inference engine, the core of the system adopts production system which is well used on artificial intelligence, and is coded by Prolog as the artificial intelligence oriented language. At present, 176 rules for inference have been registered on this system. The system will be able to presume better if more rules are added to the system.

Computerized systems for on-line management of failures: a state-of-the-art discussion of alarm systems and diagnostic systems applied in the nuclear industry

International Nuclear Information System (INIS)

Kim, I.S.

1994-01-01

It is now well perceived in the nuclear industry that improving plant information systems is vital for enhancing the operational safety of nuclear power plants. Considerable work is underway worldwide to support operators' decision-making, particularly in their difficult tasks of managing process anomalies on-line. The work includes development of (1) advanced alarm systems, such as various kinds of computer-based alarm processing systems, Critical Function Monitoring System, Success Path Monitoring System and Safety Assessment System II, and (2) real-timer diagnostic systems, such as Disturbance Analysis System, Maryland Operator Advisory System II, Model-Integrated Diagnostic Analysis System, Diagnosis System using Knowledge Engineering Technique, Detailed Diagnosis, and Operator Advisor System. This paper presents a state-of-the-art review of plant information systems for on-line management of failures in nuclear power plants, focusing on the methodological features of computerized alarm systems and diagnostic systems. (author)

Gated integrator PXI-DAQ system for Thomson scattering diagnostics

Energy Technology Data Exchange (ETDEWEB)

Patel, Kiran, E-mail: kkpatel@ipr.res.in; Pillai, Vishal; Singh, Neha; Thomas, Jinto; Kumar, Ajai

2017-06-15

Gated Integrator (GI) PXI based data acquisition (DAQ) system has been designed and developed for the ease of acquiring fast Thomson Scattered signals (∼50 ns pulse width). The DAQ system consists of in-house designed and developed GI modules and PXI-1405 chassis with several PXI-DAQ modules. The performance of the developed system has been validated during the SST-1 campaigns. The dynamic range of the GI module depends on the integrating capacitor (C{sub i}) and the modules have been calibrated using 12 pF and 27 pF integrating capacitors. The developed GI module based data acquisition system consists of sixty four channels for simultaneous sampling using eight PXI based digitization modules having eight channels per module. The error estimation and functional tests of this unit are carried out using standard source and also with the fast detectors used for Thomson scattering diagnostics. User friendly Graphical User Interface (GUI) has been developed using LabVIEW on Windows platform to control and acquire the Thomson scattering signal. A robust, easy to operate and maintain with low power consumption, having higher dynamic range with very good sensitivity and cost effective DAQ system is developed and tested for the SST-1 Thomson scattering diagnostics.

Time-frequency distributions for propulsion-system diagnostics

Science.gov (United States)

Griffin, Michael E.; Tulpule, Sharayu

1991-12-01

The Wigner distribution and its smoothed versions, i.e., Choi-Williams and Gaussian kernels, are evaluated for propulsion system diagnostics. The approach is intended for off-line kernel design by using the ambiguity domain to select the appropriate Gaussian kernel. The features produced by the Wigner distribution and its smoothed versions correlate remarkably well with documented failure indications. The selection of the kernel on the other hand is very subjective for our unstructured data.

Multi-dimensional diagnostics of high power ion beams by Arrayed Pinhole Camera System

International Nuclear Information System (INIS)

Yasuike, K.; Miyamoto, S.; Shirai, N.; Akiba, T.; Nakai, S.; Imasaki, K.; Yamanaka, C.

1993-01-01

The authors developed multi-dimensional beam diagnostics system (with spatially and time resolution). They used newly developed Arrayed Pinhole Camera (APC) for this diagnosis. The APC can get spatial distribution of divergence and flux density. They use two types of particle detectors in this study. The one is CR-39 can get time integrated images. The other one is gated Micro-Channel-Plate (MCP) with CCD camera. It enables time resolving diagnostics. The diagnostics systems have resolution better than 10mrad divergence, 0.5mm spatial resolution on the objects respectively. The time resolving system has 10ns time resolution. The experiments are performed on Reiden-IV and Reiden-SHVS induction linac. The authors get time integrated divergence distributions on Reiden-IV proton beam. They also get time resolved image on Reiden-SHVS

Algorithmic acquisition of diagnostic patterns in district heating billing system

International Nuclear Information System (INIS)

Kiluk, Sebastian

2012-01-01

An application of algorithmic exploration of billing data is examined for fault detection, diagnosis (FDD) based on evaluation of present state and detection of unexpected changes in energy efficiency of buildings. Large data sets from district heating (DH) billing systems are used for construction of feature space, diagnostic rules and classification of the buildings according to their energy efficiency properties. The algorithmic approach automates discovering knowledge about common, thus accepted changes in buildings’ properties, in equipment and in habitants’ behavior reflecting progress in technology and life style. In this article implementation of Data Mining and Knowledge Discovery (DMKD) method in supervision system with exemplary results based on real data is presented. Crucial steps of data processing influencing diagnostic results are described in details.

MFTF supervisory control and diagnostics system hardware

International Nuclear Information System (INIS)

Butner, D.N.

1979-01-01

The Supervisory Control and Diagnostics System (SCDS) for the Mirror Fusion Test Facility (MFTF) is a multiprocessor minicomputer system designed so that for most single-point failures, the hardware may be quickly reconfigured to provide continued operation of the experiment. The system is made up of nine Perkin-Elmer computers - a mixture of 8/32's and 7/32's. Each computer has ports on a shared memory system consisting of two independent shared memory modules. Each processor can signal other processors through hardware external to the shared memory. The system communicates with the Local Control and Instrumentation System, which consists of approximately 65 microprocessors. Each of the six system processors has facilities for communicating with a group of microprocessors; the groups consist of from four to 24 microprocessors. There are hardware switches so that if an SCDS processor communicating with a group of microprocessors fails, another SCDS processor takes over the communication

Artificial intelligence system for technical diagnostics of photomasks

Directory of Open Access Journals (Sweden)

Kozin A. A.

2012-02-01

Full Text Available The developed artificial intelligence system has a high level of noise immunity, so its inclusion in the hardware and software for technical diagnostics of photomasks will reduce the hardware requirements for its execution, and thereby reduce the cost of the complex. As a result it will allow to make a small-scale production profitable.

In-operation diagnostic system for reactor coolant pump

International Nuclear Information System (INIS)

Sugiyama, Mitsunobu; Hasegawa, Ichiro; Kitahara, Hiromichi; Shimamura, Kazuo; Yasuda, Chiaki; Ikeda, Yasuhiro; Kida, Yasuo.

1996-01-01

A reactor coolant pump (RCP) is one of the most important rotating machines in the primary loop nuclear power plants. To improve the reliability and of nuclear power plants, a new diagnostic system that enables early detection of RCP faults has been developed. This system is based on continuous monitoring of vibration and other process data. Vibration is an important indicator of mechanical faults providing information on physical phenomena such as changes in dynamic characteristics and excitation forces changes that signal failure or incipient failure. This new system features comparative vibration analysis and simulation to anticipate equipment failure. (author)

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

Science.gov (United States)

Lu, Liang; Qi, Lin; Luo, Yisong; Jiao, Hengchao; Dong, Junyu

2018-01-01

Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods. PMID:29498703

Is interstellar detection of higher members of the linear radicals CnCH and CnN feasible?

Science.gov (United States)

Pauzat, F.; Ellinger, Y.; Mclean, A. D.

1991-01-01

Rotational constants and dipole moments for linear-chain radicals CnCH and CnN are estimated using a combinatiaon of ab initio molecular orbital calculations and observed data on the starting members of the series. CnCH with n = 0-5 have been observed by radioastronomy in carbon-rich interstellar clouds; higher members of the series have 2Pi ground states with large dipole moments and are strong candidates for observation. CN and C3N have also been observed by radioastronomy; higher members of the series, with the possible exception of C5N, have 2Pi ground states with near-zero dipole moments making their interstellar detection hopeless under present observational conditions. C5N can be a strong candidate only if it has a 2Sigma ground state, and best computations so far indicate that this is not the case.

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

Directory of Open Access Journals (Sweden)

Liang Lu

2018-03-01

Full Text Available Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods.

Millimeter-wave imaging diagnostics systems on the EAST tokamak (invited)

Energy Technology Data Exchange (ETDEWEB)

Zhu, Y. L.; Xie, J. L., E-mail: jlxie@ustc.edu.cn; Yu, C. X.; Zhao, Z. L.; Gao, B. X.; Chen, D. X.; Liu, W. D.; Liao, W.; Qu, C. M.; Luo, C. [School of Physics, University of Science and Technology of China, Anhui 230026 (China); Hu, X.; Spear, A. G.; Luhmann, N. C.; Domier, C. W.; Chen, M.; Ren, X. [University of California, Davis, California 95616 (United States); Tobias, B. J. [Princeton Plasma Physics Laboratory, Princeton, New Jersey 08543 (United States)

2016-11-15

Millimeter-wave imaging diagnostics, with large poloidal span and wide radial range, have been developed on the EAST tokamak for visualization of 2D electron temperature and density fluctuations. A 384 channel (24 poloidal × 16 radial) Electron Cyclotron Emission Imaging (ECEI) system in F-band (90-140 GHz) was installed on the EAST tokamak in 2012 to provide 2D electron temperature fluctuation images with high spatial and temporal resolution. A co-located Microwave Imaging Reflectometry (MIR) will be installed for imaging of density fluctuations by December 2016. This “4th generation” MIR system has eight independent frequency illumination beams in W-band (75-110 GHz) driven by fast tuning synthesizers and active multipliers. Both of these advanced millimeter-wave imaging diagnostic systems have applied the latest techniques. A novel design philosophy “general optics structure” has been employed for the design of the ECEI and MIR receiver optics with large aperture. The extended radial and poloidal coverage of ECEI on EAST is made possible by innovations in the design of front-end optics. The front-end optical structures of the two imaging diagnostics, ECEI and MIR, have been integrated into a compact system, including the ECEI receiver and MIR transmitter and receiver. Two imaging systems share the same mid-plane port for simultaneous, co-located 2D fluctuation measurements of electron density and temperature. An intelligent remote-control is utilized in the MIR electronics systems to maintain focusing at the desired radial region even with density variations by remotely tuning the probe frequencies in about 200 μs. A similar intelligent technique has also been applied on the ECEI IF system, with remote configuration of the attenuations for each channel.

Hyperchaos of four state autonomous system with three positive Lyapunov exponents

International Nuclear Information System (INIS)

Ge Zhengming; Yang, C-H.

2009-01-01

This Letter gives the results of numerical simulations of Quantum Cellular Neural Network (Quantum-CNN) autonomous system with four state variables. Three positive Lyapunov exponents confirm hyperchaotic nature of its dynamics

Development of a rule-based diagnostic platform on an object-oriented expert system shell

International Nuclear Information System (INIS)

Wang, Wenlin; Yang, Ming; Seong, Poong Hyun

2016-01-01

Highlights: • Multilevel Flow Model represents system knowledge as a domain map in expert system. • Rule-based fault diagnostic expert system can identify root cause via a causal chain. • Rule-based fault diagnostic expert system can be used for fault simulation training. - Abstract: This paper presents the development and implementation of a real-time rule-based diagnostic platform. The knowledge is acquired from domain experts and textbooks and the design of the fault diagnosis expert system was performed in the following ways: (i) establishing of corresponding classes and instances to build the domain map, (ii) creating of generic fault models based on events, and (iii) building of diagnostic reasoning based on rules. Knowledge representation is a complicated issue of expert systems. One highlight of this paper is that the Multilevel Flow Model has been used to represent the knowledge, which composes the domain map within the expert system as well as providing a concise description of the system. The developed platform is illustrated using the pressure safety system of a pressurized water reactor as an example of the simulation test bed; the platform is developed using the commercial and industrially validated software G2. The emulation test was conducted and it has been proven that the fault diagnosis expert system can identify the faults correctly and in a timely way; this system can be used as a simulation-based training tool to assist operators to make better decisions.

Protection study of a diagnostic system for electron beam at the output of an accelerator

International Nuclear Information System (INIS)

Rahmani, Kaouther; Yaacoubi, Imen

2009-01-01

The aim of this work is the determination of the conception of a protection system dedicated to protect a diagnostic system in the CNSTN. According to this study, the suitable material for the protection against the electrons in the plexiglas and the supermalloy to protect the future diagnostic system against the magnetic field. (Author)

The graphics-based human interface to the DISYS diagnostic/control guidance system at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Chavez, C.; Kamarthi, S.; Dharap, S.; Lindsay, R.W.; Staffon, J.

1990-01-01

An initial graphics based interface to the real-time DISYS diagnostic system has been developed using the multi-tasking capabilities of the UNIX operating system and X-Windows 11 Xlib graphics library. This system is interfaced to live plant data at the Experimental Breeder Reactor (EBR-2) for the Argon Cooling System of fuel handling operations and the steam plant. The interface includes an intelligent process schematic which highlights problematic components and sensors based on the results of the diagnostic computations. If further explanation of a faulted component is required, the user can call up a display of the diagnostic computations presented in a tree-like diagram. Numerical data on the process schematic and optional diagnostic tree are updated as new real-time data becomes available. The initial X-Windows 11 based interface will be further enhanced using VI Corporation DATAVIEWS graphical data base software. 5 refs., 6 figs

Development of an Adaptable Display and Diagnostic System for the Evaluation of Tropical Cyclone Forecasts

Science.gov (United States)

Kucera, P. A.; Burek, T.; Halley-Gotway, J.

2015-12-01

NCAR's Joint Numerical Testbed Program (JNTP) focuses on the evaluation of experimental forecasts of tropical cyclones (TCs) with the goal of developing new research tools and diagnostic evaluation methods that can be transitioned to operations. Recent activities include the development of new TC forecast verification methods and the development of an adaptable TC display and diagnostic system. The next generation display and diagnostic system is being developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and broader TC research community. The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models. The system is built upon modern and flexible technology that includes OpenLayers Mapping tools that are platform independent. The forecast track and intensity along with associated observed track information are stored in an efficient MySQL database. The system provides easy-to-use interactive display system, and provides diagnostic tools to examine forecast track stratified by intensity. Consensus forecasts can be computed and displayed interactively. The system is designed to display information for both real-time and for historical TC cyclones. The display configurations are easily adaptable to meet the needs of the end-user preferences. Ongoing enhancements include improving capabilities for stratification and evaluation of historical best tracks, development and implementation of additional methods to stratify and compute consensus hurricane track and intensity forecasts, and improved graphical display tools. The display is also being enhanced to incorporate gridded forecast, satellite, and sea surface temperature fields. The presentation will provide an overview of the display and diagnostic system development and demonstration of the current capabilities.

Some aspects of the Unilac beam diagnostic system

International Nuclear Information System (INIS)

Glatz, J.; Klabunde, J.; Strehl, P.

1976-01-01

A survey of the Unilac beam diagnostic system is given with special reference to the operational experience collected during the running-in period. Devices for measurement and display of beam profiles, energy, rf phases and amplitudes and rf matching procedures between the different stages of the accelerator are described. Some aspects concerning future developments and improvements are discussed briefly. (author)

A Diagnostic System for Speed-Varying Motor Rotary Faults

Directory of Open Access Journals (Sweden)

Chwan-Lu Tseng

2014-01-01

Full Text Available This study proposed an intelligent rotary fault diagnostic system for motors. A sensorless rotational speed detection method and an improved dynamic structural neural network are used. Moreover, to increase the convergence speed of training, a terminal attractor method and a hybrid discriminant analysis are also adopted. The proposed method can be employed to detect the rotary frequencies of motors with varying speeds and can enhance the discrimination of motor faults. To conduct the experiments, this study used wireless sensor nodes to transmit vibration data and employed MATLAB to write codes for functional modules, including the signal processing, sensorless rotational speed estimation, neural network, and stochastic process control chart. Additionally, Visual Basic software was used to create an integrated human-machine interface. The experimental results regarding the test of equipment faults indicated that the proposed novel diagnostic system can effectively estimate rotational speeds and provide superior ability of motor fault discrimination with fast training convergence.

Feedback control and beam diagnostic algorithms for a multiprocessor DSP system

International Nuclear Information System (INIS)

Teytelman, D.; Claus, R.; Fox, J.; Hindi, H.; Linscott, I.; Prabhakar, S.

1996-09-01

The multibunch longitudinal feedback system developed for use by PEP-II, ALS and DAΦNE uses a parallel array of digital signal processors to calculate the feedback signals from measurements of beam motion. The system is designed with general-purpose programmable elements which allow many feedback operating modes as well as system diagnostics, calibrations and accelerator measurements. The overall signal processing architecture of the system is illustrated. The real-time DSP algorithms and off-line postprocessing tools are presented. The problems in managing 320 K samples of data collected in one beam transient measurement are discussed and the solutions are presented. Example software structures are presented showing the beam feedback process, techniques for modal analysis of beam motion(used to quantify growth and damping rates of instabilities) and diagnostic functions (such as timing adjustment of beam pick-up and kicker components). These operating techniques are illustrated with example results obtained from the system installed at the Advanced Light Source at LBL

Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

Science.gov (United States)

Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

2007-12-15

Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Diagnostic systems in nuclear power plants. Proceedings of a technical committee meeting. Working material

International Nuclear Information System (INIS)

1998-01-01

Nuclear power industry has a quite long tradition in on-line diagnostic of mechanical components and a considerable effort was put in developing diagnostic systems which are able to detect arising mechanical problems at an early stage. Computers are increasingly exploited to provide higher level information on process behaviour such as: early indication of the process deviation from normal conditions; rapid identification of the cause of any disturbance; prediction of the evolution of a disturbance; operator aid through computerized help. Following the recommendation of Several Member States to strengthen the activity in this field two divisions of IAEA established in 1995 the International Task Force on Nuclear Power Plant Diagnostics. The scope of the task force cover both technological developments and safety/licensing aspects of diagnostics. This report contains papers presented at the last in the series of Technical Committee Meetings on the Diagnostic Systems in Nuclear Power Plants organized in the framework of International Task Force

Diagnostic systems in nuclear power plants. Proceedings of a technical committee meeting. Working material

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-12-31

Nuclear power industry has a quite long tradition in on-line diagnostic of mechanical components and a considerable effort was put in developing diagnostic systems which are able to detect arising mechanical problems at an early stage. Computers are increasingly exploited to provide higher level information on process behaviour such as: early indication of the process deviation from normal conditions; rapid identification of the cause of any disturbance; prediction of the evolution of a disturbance; operator aid through computerized help. Following the recommendation of Several Member States to strengthen the activity in this field two divisions of IAEA established in 1995 the International Task Force on Nuclear Power Plant Diagnostics. The scope of the task force cover both technological developments and safety/licensing aspects of diagnostics. This report contains papers presented at the last in the series of Technical Committee Meetings on the Diagnostic Systems in Nuclear Power Plants organized in the framework of International Task Force Refs, figs, tabs

Trouble diagnostic system for pumps used in thermal and nuclear power plant

International Nuclear Information System (INIS)

Amano, K.; Hayashi, M.; Takagi, M.; Katsura, H.

1995-01-01

Most power plants have been operated under severe conditions to meet the diversification in electricity supply and demand. Therefore, it has become an important objective to keep the pumps under maintenance and control which necessitates a more reliable diagnostic system. With this in mind, the authors set out to perform the simulation tests of abnormal operation using a model pump, and have developed the diagnostic system for pumps based on vibration and process data. The main features of the system are 1) parallel processing of data acquisition and the diagnosis and 2) guidance function for the abnormal operation. The system has been applied to an actual pump to detect a bearing damage and set up at the nuclear power plant. (author)

Control philosophy and diagnostic systems of Superconducting Cyclotron

International Nuclear Information System (INIS)

Roy, Anindya; Bhattacharjee, Tanushyam; Chaddha, N.; Bhole, R.B.; Pal, Sarbajit; Samanta, N.C.; Dutta, C.D.; Mukhopadhyay, B.; Panda, U.S.; Sarkar, B.; Nabhiraj, P.Y.; Sarkar, D.

2009-01-01

The control system has the primary task of monitoring and control of all the important parameters of a machine comprises of various sub-systems. The paper describes the philosophy of the distributed control system of Superconducting Cyclotron implemented with the support of reliable and fast control network. The paper also describes the field hardware interfaced with various software platforms at different levels of individual sub-systems e.g. Main Magnet Power Supply, Trim-coil Power Supplies, He Liquefier/Refrigerator Plant, Cryogen Delivery System, RF System, ECR Ion source, Vacuum System, Radiation Monitoring System, Alarm Annunciation System, LCW System of SC Cyclotron. The database management system facilitating the exchange of control data among the sub-systems, serving as primary source of information to understand the behavior of the cyclotron, is also discussed. A brief description of various beam diagnostic instruments and their respective control systems e.g. Main Probe, Borescope, Beam viewer, Magnetic channel control system, Beam line slit control system, are briefly described. (author)

DIAGNOSTICS OF DISORDERS AND DISEASES OF MUSCULOSKELETAL SYSTEM IN SCHOOLCHILDREN: APPROACHES, TERMINOLOGY, CLASSIFICATION

Directory of Open Access Journals (Sweden)

N.B. Mirskaya

2009-01-01

Full Text Available This article describes an information system for physicians working in general education institutes, which is named «Detection, correction and prophylaxis of musculoskeletal system disorders in students of general education institutes». This system was created for the purpose of improving diagnostics of initial stages of musculoskeletal system in schoolchildren, detecting of risk factors, and for the provision of timely prophylaxis during school education. The system was based on classification of functional disorders and initial stages of diseases of musculoskeletal system in schoolchildren, developed by authors of present article, and methods of medical examination and organization of this work.Key words: schoolchildren, musculoskeletal system, diagnostics, classification, prophylaxis.(Voprosy sovremennoi pediatrii — Current Pediatrics. 2009;8(3:10-13

[Coronary angioplasty with the Monorail system via 6 French diagnostic catheters].

Science.gov (United States)

Villavicencio, R; González, H; López, J; Zavala, E; Ban Hayashi, E B; Gaspar, J; Gil, M; Martínez Ríos, M A

1994-01-01

We studied the use of "Monorail" system with Express (Scimed) balloon catheters for coronary angioplasty through 6 French (F) "high-flow" diagnostic catheters (Novoste, USCI). Prospectively, from July 1992 to January 1993, angioplasty of 31 lesions in 24 patients was attempted (1.3 lesions/procedure). Twenty procedures were of a single lesion and four were multi-vessel angioplasty. Fourteen lesions were in the left anterior descending or in its branches, 10 in the left circumflex or in its branches, 6 in the right coronary artery, and one in the distal anastomosis of an internal mammary artery graft. Thirteen lesions (42%) were type A, 17 (55%) type B and one (3%) type C. Balloon sizes varied between 2.0 and 3.5 mm. Twenty-nine lesions could be successfully dilated (93.5%); two cases were unsuccessful due to an acute occlusion in one and residual stenosis of more than 50% in the other. For only one case, another balloon catheter different from the "Monorail" system was necessary to complete a multi-vessel angioplasty. Coronary visualization and manipulation of the balloon through the tip of the diagnostic catheter were satisfactory in all cases, except with the 3.5 mm balloon catheter. Coronary angioplasty with "Monorail" system balloon catheters through 6 F "high-flow" diagnostic catheters is feasible and provides a high success rate in simple and moderately complex selected lesions, including multivessel angioplasty with advantages of smaller artery punction and the feasibility of performing coronary angioplasty with the same catheter used for diagnostic angiography.

Cooperative research and development for artificial intelligence based reactor diagnostic system

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Abboud, R.G.; Chasensky, T.M.

1994-01-01

Artificial Intelligence (AI) techniques in the form of knowledge-based Expert Systems (ESs) have been proposed to provide on-line decision-making support for plant operators during both normal and emergency conditions. However, in spite of the great interest in these advanced techniques, their application in the diagnosis of large-scale processes has not yet reached its full potential because of limitations of the knowledge base. These limitations include problems with knowledge acquisition and the use of an event-oriented approach for process diagnosis. To investigate the capabilities of this two-level hierarchical knowledge structure, Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL)are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA) project to perform feasibility studies on the proposed diagnostic system. Investigations are being performed in the construction of a physics-based plant level process diagnostic ES and the characterization of component-level fault project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use T-H signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance. To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. This is an ongoing multi-year project and the remainder of this paper presents a mid-term status report

A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Bao Xue-Mei

2013-01-01

In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network (CNN). The employed CNN comprises a 4-neuron layer called a chaotic neuron layer (CNL), where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice (CML), which can be easily implemented in parallel by hardware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosystem is secure and practical, and suitable for image encryption. (general)

ITER diagnostics ex-vessel engineering services

Energy Technology Data Exchange (ETDEWEB)

Arumugam, A.P., E-mail: arun.prakash@iter.org; Walker, C.I.; Andrew, P.; Barnsley, R.; Beltran, D.; Bertalot, L.; Dammann, A.; Direz, M.F.; Drevon, J.M.; Encheva, A.; Giacomin, T.; Hourtoule, J.; Kuehn, I.; Lanza, R.; Levesy, B.; Maquet, P.; Patel, K.M.; Patisson, L.; Pitcher, C.S.; Portales, M.; and others

2013-10-15

Highlights: • This paper describes about the ITER diagnostics ex-vessel engineering services. • It describes various diagnostics systems, its location and its environment. • Diagnostics interfaces with other services such as the buildings, HVAC, electrical services, cooling water, vacuum, liquid and gas distribution. • All the interfaces with these services are identified and defined. • Buildings services for diagnostics, such as penetrations, local shielding, embedment and temperature control are discussed. -- Abstract: Extensive diagnostics systems will be installed on the ITER machine to provide the measurements necessary to control, evaluate and optimize plasma performance in ITER and to further the understanding of plasma physics. These include measurements of temperature, density, impurity concentration, and particle and energy confinement times. ITER diagnostic systems extend from the center of the Tokamak to the various diagnostic areas, where they are controlled and acquired data is processed. This mainly includes the areas such as ports, port cells, gallery, diagnostics enclosures and cubicle areas. The diagnostics port plugs encloses the front end of the diagnostic systems and the diagnostics building houses the diagnostics equipment, instrumentation and control cubicles. There are several systems providing services to diagnostics. These mainly include ITER buildings, electrical power services, cooling water services, Heating Ventilation and Air Conditioning (HVAC), vacuum services, liquid and gas distribution services, cable engineering, de-tritiation systems, control cubicles, etc. Requirements of these service systems have to be defined, even though many of the diagnostics are at an early stage of development. It is a real challenge to define and to design diagnostics systems considering the constraints imposed by these service systems. This paper summarizes the provision of these services to the individual diagnostics and diagnostics areas

ITER diagnostics ex-vessel engineering services

International Nuclear Information System (INIS)

Arumugam, A.P.; Walker, C.I.; Andrew, P.; Barnsley, R.; Beltran, D.; Bertalot, L.; Dammann, A.; Direz, M.F.; Drevon, J.M.; Encheva, A.; Giacomin, T.; Hourtoule, J.; Kuehn, I.; Lanza, R.; Levesy, B.; Maquet, P.; Patel, K.M.; Patisson, L.; Pitcher, C.S.; Portales, M.

2013-01-01

Highlights: • This paper describes about the ITER diagnostics ex-vessel engineering services. • It describes various diagnostics systems, its location and its environment. • Diagnostics interfaces with other services such as the buildings, HVAC, electrical services, cooling water, vacuum, liquid and gas distribution. • All the interfaces with these services are identified and defined. • Buildings services for diagnostics, such as penetrations, local shielding, embedment and temperature control are discussed. -- Abstract: Extensive diagnostics systems will be installed on the ITER machine to provide the measurements necessary to control, evaluate and optimize plasma performance in ITER and to further the understanding of plasma physics. These include measurements of temperature, density, impurity concentration, and particle and energy confinement times. ITER diagnostic systems extend from the center of the Tokamak to the various diagnostic areas, where they are controlled and acquired data is processed. This mainly includes the areas such as ports, port cells, gallery, diagnostics enclosures and cubicle areas. The diagnostics port plugs encloses the front end of the diagnostic systems and the diagnostics building houses the diagnostics equipment, instrumentation and control cubicles. There are several systems providing services to diagnostics. These mainly include ITER buildings, electrical power services, cooling water services, Heating Ventilation and Air Conditioning (HVAC), vacuum services, liquid and gas distribution services, cable engineering, de-tritiation systems, control cubicles, etc. Requirements of these service systems have to be defined, even though many of the diagnostics are at an early stage of development. It is a real challenge to define and to design diagnostics systems considering the constraints imposed by these service systems. This paper summarizes the provision of these services to the individual diagnostics and diagnostics areas

Status of the Tandem Mirror Experiment-Upgrade (TMX-U) diagnostic system

International Nuclear Information System (INIS)

Coutts, G.W.; Coffield, F.E.; Hornady, R.S.

1983-01-01

This paper presents the current status of the Tandem Mirror Experiment-Upgrade (TMX-U) diagnostics system. For the initial instruments active on TMX-U, the expansions or upgrades that have been implemented are outlined. For the newly added systems, more implementation details are presented

Design Features of the Neutral Particle Diagnostic System for the ITER Tokamak

Science.gov (United States)

Petrov, S. Ya.; Afanasyev, V. I.; Melnik, A. D.; Mironov, M. I.; Navolotsky, A. S.; Nesenevich, V. G.; Petrov, M. P.; Chernyshev, F. V.; Kedrov, I. V.; Kuzmin, E. G.; Lyublin, B. V.; Kozlovski, S. S.; Mokeev, A. N.

2017-12-01

The control of the deuterium-tritium (DT) fuel isotopic ratio has to ensure the best performance of the ITER thermonuclear fusion reactor. The diagnostic system described in this paper allows the measurement of this ratio analyzing the hydrogen isotope fluxes (performing neutral particle analysis (NPA)). The development and supply of the NPA diagnostics for ITER was delegated to the Russian Federation. The diagnostics is being developed at the Ioffe Institute. The system consists of two analyzers, viz., LENPA (Low Energy Neutral Particle Analyzer) with 10-200 keV energy range and HENPA (High Energy Neutral Particle Analyzer) with 0.1-4.0MeV energy range. Simultaneous operation of both analyzers in different energy ranges enables researchers to measure the DT fuel ratio both in the central burning plasma (thermonuclear burn zone) and at the edge as well. When developing the diagnostic complex, it was necessary to account for the impact of several factors: high levels of neutron and gamma radiation, the direct vacuum connection to the ITER vessel, implying high tritium containment, strict requirements on reliability of all units and mechanisms, and the limited space available for accommodation of the diagnostic hardware at the ITER tokamak. The paper describes the design of the diagnostic complex and the engineering solutions that make it possible to conduct measurements under tokamak reactor conditions. The proposed engineering solutions provide a safe—with respect to thermal and mechanical loads—common vacuum channel for hydrogen isotope atoms to pass to the analyzers; ensure efficient shielding of the analyzers from the ITER stray magnetic field (up to 1 kG); provide the remote control of the NPA diagnostic complex, in particular, connection/disconnection of the NPA vacuum beamline from the ITER vessel; meet the ITER radiation safety requirements; and ensure measurements of the fuel isotopic ratio under high levels of neutron and gamma radiation.

Diagnostics monitor of the braking efficiency in the on board diagnostics system for the motor vehicles

Science.gov (United States)

Gajek, Andrzej

2016-09-01

The article presents diagnostics monitor for control of the efficiency of brakes in various road conditions in cars equipped with pressure sensor in brake (ESP) system. Now the brake efficiency of the vehicles is estimated periodically in the stand conditions on the base of brake forces measurement or in the road conditions on the base of the brake deceleration. The presented method allows to complete the stand - periodical tests of the brakes by current on board diagnostics system OBD for brakes. First part of the article presents theoretical dependences between deceleration of the vehicle and brake pressure. The influence of the vehicle mass, initial speed of braking, temperature of brakes, aerodynamic drag, rolling resistance, engine resistance, state of the road surface, angle of the road sloping on the deceleration have been analysed. The manner of the appointed of these parameters has been analysed. The results of the initial investigation have been presented. At the end of the article the strategy of the estimation and signalization of the irregular value of the deceleration are presented.

Is interstellar detection of higher members of the linear radicals CnCH and CnN feasible?

International Nuclear Information System (INIS)

Pauzat, F.; Ellinger, Y.; Mclean, A.D.

1991-01-01

Rotational constants and dipole moments for linear-chain radicals CnCH and CnN are estimated using a combinatiaon of ab initio molecular orbital calculations and observed data on the starting members of the series. CnCH with n = 0-5 have been observed by radioastronomy in carbon-rich interstellar clouds; higher members of the series have 2Pi ground states with large dipole moments and are strong candidates for observation. CN and C3N have also been observed by radioastronomy; higher members of the series, with the possible exception of C5N, have 2Pi ground states with near-zero dipole moments making their interstellar detection hopeless under present observational conditions. C5N can be a strong candidate only if it has a 2Sigma ground state, and best computations so far indicate that this is not the case. 20 refs

Expert System for Diagnostics and Status Monitoring of NPP Water Chemistry Condition

International Nuclear Information System (INIS)

Shvedova, M.N.; Kritski, V.G.; Zakharova, S.V.; Nikolaev, F.V.; Benediktov, V.B.

2002-01-01

Water chemistry condition (WCC) has been the subject of constant study and improvement up to the present day. It is connected with the presence of a direct relationship between the violation of water chemistry regulation on the one hand and components reliability of the circuit's equipment and cost-effectiveness of their operation on the other. It dictates the necessity to apply different optimization methods in the field of monitoring and use of information - analytical and diagnostic systems to assess WCC quality, control and support. By now NPP experts have broad experience in revealing and removing the causes of WCC disturbances. However this knowledge is often of an intuitive, non-classified nature, scattered among various working documents, which makes their transfer difficult. Based on what has been mentioned above, special attention is currently being paid to the problem of creating expert diagnostic systems for supporting the optimum WCC. The existing developments in this field (DIWA, Smart chem Works, the water quality control system at the Onagava NPP etc. [1,3,4,5] are based on wide use of experts' knowledge. Such expert diagnostic systems for supporting WCC refer to the new generation of intellectual control methods, which allow the incorporation of the latest achievements both in the field of water chemistry simulation and in the field of artificial intelligence and computer technologies. LI 'VNIPIET' employees have, for several years, been developing an expert diagnostic system for supporting WCC and status monitoring of RBMK - reactor NPPs [2]. This system has not only conveniently organized the traditional functions of information acquisition and storage, a complete presentation of information in the form of tables, graphs of a dynamical changes of parameters and formation regular reports, diagnostic functions and issuing recommendations on WCC correction, but it also allows the assessment of confidence in the diagnosis made, relying on a wide

Improvements to a high-frequency fiber-optic system for plasma diagnostics

International Nuclear Information System (INIS)

Ogle, J.W.; Lyons, P.B.; Looney, L.; Hocker, L.; Nelson, M.A.; Zagarino, P.A.; Davies, T.J.; Simmons, R.D.; Selk, R.; Hopkins, B.

1981-01-01

A system for high-frequency recording of plasma diagnostics has previously been reported. Substantial improvements have been made in the system response, dynamic range, and calibration of the system. Plastic-clad silica fiber is used as a radiation-to-light converter using the Cerenkov process. A spectral equalizer device is used to compensate for the material dispersion in the fiber, increasing the frequency response (approx. = 1 GHz-km) and the dynamic range (a factor of > 20 over a FWHM 1 nm, 50% transmitting interference filter). The calibration system uses a pulsed injection laser diode (< 100 ps FWHM) injected into the fiber at the radiation end of the fiber and detected by a microchannel plate photomultiplier tube on the recording end. The injection laser diode is triggered by a synchronous trigger delay unit, which also triggers a sampling or real time scope after as much as 10 μs delay with < 50 ps jitter. The system improvements are described in detail and the utility of these components in other plasma diagnostic systems is discussed

Plasma Diagnostics

Energy Technology Data Exchange (ETDEWEB)

Zaveryaev, V [Kurchatov Institute, Moscow (Russian Federation); others, and

2012-09-15

The success in achieving peaceful fusion power depends on the ability to control a high temperature plasma, which is an object with unique properties, possibly the most complicated object created by humans. Over years of fusion research a new branch of science has been created, namely plasma diagnostics, which involves knowledge of almost all fields of physics, from electromagnetism to nuclear physics, and up-to-date progress in engineering and technology (materials, electronics, mathematical methods of data treatment). Historically, work on controlled fusion started with pulsed systems and accordingly the methods of plasma parameter measurement were first developed for short lived and dense plasmas. Magnetically confined hot plasmas require the creation of special experimental techniques for diagnostics. The diagnostic set is the most scientifically intensive part of a plasma device. During many years of research operation some scientific tasks have been solved while new ones arose. New tasks often require significant changes in the diagnostic system, which is thus a very flexible part of plasma machines. Diagnostic systems are designed to solve several tasks. As an example here are the diagnostic tasks for the International Thermonuclear Experimental Reactor - ITER: (1) Measurements for machine protection and basic control; (2) Measurements for advanced control; (3) Additional measurements for performance evaluation and physics. Every new plasma machine is a further step along the path to the main goal - controlled fusion - and nobody knows in advance what new phenomena will be met on the way. So in the planning of diagnostic construction we should keep in mind further system upgrading to meet possible new scientific and technical challenges. (author)

A study of diagnostics expert system for accelerator applications

International Nuclear Information System (INIS)

Tyagi, Y.; Banerji, Anil; Kotaiah, S.

2003-01-01

Knowledge based techniques are proving to be useful in a number of problem domains which typically requires human expertise. Expert systems employing knowledge based techniques are a recent product of artificial intelligence. Methods developed in the artificial intelligence area can be applied with success for certain classes of problems in accelerator. Accelerators are complex devices with thousands of components. The number of possible faults or problems that can appear is enormous. A diagnostics expert system can provide great help in finding and diagnosing problems in Indus-II accelerator sub-systems. (author)

Conceptual design of neutron diagnostic systems for fusion experimental reactor

International Nuclear Information System (INIS)

Iguchi, T.; Kaneko, J.; Nakazawa, M.

1994-01-01

Neutron measurement in fusion experimental reactors is very important for burning plasma diagnostics and control, monitoring of irradiation effects on device components, neutron source characterization for in-situ engineering tests, etc. A conceptual design of neutron diagnostic systems for an ITER-like fusion experimental reactor has been made, which consists of a neutron yield monitor, a neutron emission profile monitor and a 14-MeV spectrometer. Each of them is based on a unique idea to meet the required performances for full power conditions assumed at ITER operation. Micro-fission chambers of 235 U (and 238 U) placed at several poloidal angles near the first wall are adopted as a promising neutron yield monitor. A collimated long counter system using a 235 U fission chamber and graphite neutron moderators is also proposed to improve the calibration accuracy of absolute neutron yield determination

Evaluation of Diagnostic Systems: The Selection of Students at Risk of Academic Difficulties

Science.gov (United States)

Smolkowski, Keith; Cummings, Kelli D.

2015-01-01

Diagnostic tools can help schools more consistently and fairly match instructional resources to the needs of their students. To ensure the best educational outcome for each child, diagnostic decision-making systems seek to balance time, clarity, and accuracy. However, recent research notes that many educational decisions tend to be made using…

Diagnostic model for assessing traceability system performance in fish processing plants

NARCIS (Netherlands)

Mgonja, J.T.; Luning, P.A.; Vorst, van der J.G.A.J.

2013-01-01

This paper introduces a diagnostic tool that can be used by fish processing companies to evaluate their own traceability systems in a systematic manner. The paper begins with discussions on the rationale of traceability systems in food manufacturing companies, followed by a detailed analysis of the

System for simulating fluctuation diagnostics for application to turbulence computations

International Nuclear Information System (INIS)

Bravenec, R.V.; Nevins, W.M.

2006-01-01

Present-day nonlinear microstability codes are able to compute the saturated fluctuations of a turbulent fluid versus space and time, whether the fluid be liquid, gas, or plasma. They are therefore able to determine turbulence-induced fluid (or particle) and energy fluxes. These codes, however, must be tested against experimental data not only with respect to transport but also characteristics of the fluctuations. The latter is challenging because of limitations in the diagnostics (e.g., finite spatial resolution) and the fact that the diagnostics typically do not measure exactly the quantities that the codes compute. In this work, we present a system based on IDL registered analysis and visualization software in which user-supplied 'diagnostic filters' are applied to the code outputs to generate simulated diagnostic signals. The same analysis techniques as applied to the measurements, e.g., digital time-series analysis, may then be applied to the synthesized signals. Their statistical properties, such as rms fluctuation level, mean wave numbers, phase and group velocities, correlation lengths and times, and in some cases full S(k,ω) spectra, can then be compared directly to those of the measurements

Mechanical fault diagnostics for induction motor with variable speed drives using Adaptive Neuro-fuzzy Inference System

Energy Technology Data Exchange (ETDEWEB)

Ye, Z. [Department of Electrical & amp; Computer Engineering, Queen' s University, Kingston, Ont. (Canada K7L 3N6); Sadeghian, A. [Department of Computer Science, Ryerson University, Toronto, Ont. (Canada M5B 2K3); Wu, B. [Department of Electrical & amp; Computer Engineering, Ryerson University, Toronto, Ont. (Canada M5B 2K3)

2006-06-15

A novel online diagnostic algorithm for mechanical faults of electrical machines with variable speed drive systems is presented in this paper. Using Wavelet Packet Decomposition (WPD), a set of feature coefficients, represented with different frequency resolutions, related to the mechanical faults is extracted from the stator current of the induction motors operating over a wide range of speeds. A new integrated diagnostic system for electrical machine mechanical faults is then proposed using multiple Adaptive Neuro-fuzzy Inference Systems (ANFIS). This paper shows that using multiple ANFIS units significantly reduces the scale and complexity of the system and speeds up the training of the network. The diagnostic algorithm is validated on a three-phase induction motor drive system, and it is proven to be capable of detecting rotor bar breakage and air gap eccentricity faults with high accuracy. The algorithm is applicable to a variety of industrial applications where either continuous on-line monitoring or off-line fault diagnostics is required. (author)

Final design of thermal diagnostic system in SPIDER ion source

Energy Technology Data Exchange (ETDEWEB)

Brombin, M., E-mail: matteo.brombin@igi.cnr.it; Dalla Palma, M.; Pasqualotto, R.; Pomaro, N. [Consorzio RFX, Corso Stati Uniti 4, I-35127 Padova (Italy)

2016-11-15

The prototype radio frequency source of the ITER heating neutral beams will be first tested in SPIDER test facility to optimize H{sup −} production, cesium dynamics, and overall plasma characteristics. Several diagnostics will allow to fully characterise the beam in terms of uniformity and divergence and the source, besides supporting a safe and controlled operation. In particular, thermal measurements will be used for beam monitoring and system protection. SPIDER will be instrumented with mineral insulated cable thermocouples, both on the grids, on other components of the beam source, and on the rear side of the beam dump water cooled elements. This paper deals with the final design and the technical specification of the thermal sensor diagnostic for SPIDER. In particular the layout of the diagnostic, together with the sensors distribution in the different components, the cables routing and the conditioning and acquisition cubicles are described.

Final design of thermal diagnostic system in SPIDER ion source

International Nuclear Information System (INIS)

Brombin, M.; Dalla Palma, M.; Pasqualotto, R.; Pomaro, N.

2016-01-01

The prototype radio frequency source of the ITER heating neutral beams will be first tested in SPIDER test facility to optimize H"− production, cesium dynamics, and overall plasma characteristics. Several diagnostics will allow to fully characterise the beam in terms of uniformity and divergence and the source, besides supporting a safe and controlled operation. In particular, thermal measurements will be used for beam monitoring and system protection. SPIDER will be instrumented with mineral insulated cable thermocouples, both on the grids, on other components of the beam source, and on the rear side of the beam dump water cooled elements. This paper deals with the final design and the technical specification of the thermal sensor diagnostic for SPIDER. In particular the layout of the diagnostic, together with the sensors distribution in the different components, the cables routing and the conditioning and acquisition cubicles are described.

Problems of steam turbine diagnostics and the 'Simens' diagnosis system

International Nuclear Information System (INIS)

Tserner, V.; Andrea, K.

1993-01-01

Diagnostics system, allowing one to detect changes in the state on single turbine elements at an early stage is described. Besides this system allows one to utilize the turbine plant optimally and efficiency from the viewpoint of the equipment durability. Specially oriented monitoring of the turbine plant and equipment element state saves resources necessary to keep up the working order of the equipment

Video profile monitor diagnostic system for GTA

International Nuclear Information System (INIS)

Sandoval, D.P.; Garcia, R.C.; Gilpatrick, J.D.; Johnson, K.F.; Shinas, M.A.; Wright, R.; Yuan, V.; Zander, M.E.

1992-01-01

This paper describes a video diagnostic system used to measure the beam profile and position of the Ground Test Accelerator 2.5-MeV H - ion beam as it exits the intermediate matching section. Inelastic collisions between H-ions and residual nitrogen to fluoresce. The resulting light is captured through transport optics by an intensified CCD camera and is digitized. Real-time beam-profile images are displayed and stored for detailed analysis. Analyzed data showing resolutions for both position and profile measurements will also be presented

Quality control procedures of dental diagnostic radiology systems

International Nuclear Information System (INIS)

Andrade, Paula Serra Sasaki

2007-01-01

This work presents quality control reference procedures for dental diagnostic radiology systems, following the recommendations of the Publication 453 of the Brazilian Health Ministry (PF453), to be applied in dental clinics, in order to achieve an improvement in the radiological image qualities and the patient dose reduction. All tests were applied in an intraoral X rays system, following the methodology developed and the requirements of the PF 453. In order to verify the best quality of the image in relation to the smaller exposition time an object test was also developed in this work. The use of this object allowed the reduction of the exposition time of 0.5 seconds, the maximum value of the linear region of the characteristic curve, for 0.2 seconds. The tested X rays system showed a very good agreement with the applied procedures, detaching the reduction of the skin entrance dose using the film-holding devices. However, the size of the field increased and exceeded the maximum value of 6 cm recommended in the standard. The importance of the quality control in dental diagnostic radiology systems is essential due to the constant use of X radiation in dental clinics. The PF453 recommends the frequency of at least two years for the constancy tests. However, it is suggested that the professional, surgeon-dentist, should be responsible for the internal control of the image quality obtained from the X rays device. This can be done through monthly exposures of the object test developed in this work. (author)

Intelligent monitoring of water chemistry - Diagnostic expert system DIWATM

International Nuclear Information System (INIS)

Metzner, W.; Streit, K.

2002-01-01

For fast and comprehensive evaluation of power plant water chemistry conditions and reliable diagnosis in the event of disturbances considerable advantages are provided by employment of the Diagnostic Expert System DIWA. The interface to the process control system (I and C) and the integration of the DIWA system in the office PC network are the preconditions that DIWA operates as a monitoring system in real time. The performance of diagnosis, which are processed by a fuzzy-logic-supported knowledge base ensures not only the detection of all disturbances but also different analyses of the plant operation mode. By editing the knowledge base the Al of the system can increase without system programming. (authors)

Vacuum system design and tritium inventory for the charge exchange diagnostic on the Tokamak Fusion Test Reactor

International Nuclear Information System (INIS)

Medley, S.S.

1986-01-01

The application of charge exchange analyzers for the measurement of ion temperature in fusion plasma experiments requires a direct connection between the diagnostic and plasma-discharge vacuum chambers. Differential pumping of the gas load from the diagnostic stripping cell operated at > or approx. = 10 -3 Torr is required to maintain the analyzer chamber at a pressure of -6 Torr. The migration of gases between the diagnostic and plasma vacuum chambers must be minimized. In particular, introduction of the analyzer stripping cell gas into the plasma chamber having a base pressure of -8 Torr must be suppressed. The charge exchange diagnostic for the Tokamak Fusion Test Reactor (TFTR) is comprised of two analyzer systems designed to contain a total of 18 independent mass/energy analyzers and one diagnostic neutral beam rated at 80 keV, 15 A. The associated arrays of multiple, interconnected vacuum systems were analyzed using the Vacuum System Transient Simulator (Vsts) computer program which models the transient transport of multigas species through complex networks of ducts, valves, traps, vacuum pumps, and other related vacuum system components. In addition to providing improved design performance at reduced costs, the analysis yields estimates for the exchange of tritium from the torus to the diagnostic components and of the diagnostic working gases to the torus

'Kazmer' a complex noise diagnostic system for 1000 MWe PWR WWER type nuclear power units

International Nuclear Information System (INIS)

Por, G.

1992-06-01

Noise diagnostic systems have previously been developed and installed for the WWER-440 type reactors at the Paks Nuclear Power Plant, Hungary. Based on the experiences, the system has been extended and modified for use in 1000 MWe, WWER-1000 type units. KAZMER consists of three subsystem, the KARD reactor noise diagnostic system, ARGUS vibration monitoring system for rotation machinery, and ALMOS acoustic monitoring system. The installation of the KAZMER system at the Kalinin Nuclear Power Station, Russia, and the first operational experiences are outlined. (R.P.) 15 refs.; 9 figs

Bent CNN bond of diazo compounds, RR'(Cdbnd N+dbnd N-)

Science.gov (United States)

Akita, Motoko; Takahashi, Mai; Kobayashi, Keiji; Hayashi, Naoto; Tukada, Hideyuki

2013-02-01

The reaction of ninhydrin with benzophenone hydrazone afforded 2-diazo-3-diphenylmethylenehydrazono-1-indanone 1 and 2-diazo-1,3-bis(diphenylmethylenehydrazono)indan 2. X-ray crystal structure analyses of these products showed that the diazo functional group Cdbnd N+dbnd N- of 1 is bent by 172.9°, while that of 2 has a linear geometry. The crystal structure data of diazo compounds have been retrieved from the Cambridge Structural Database (CSD), which hit 177 entries to indicate that the angle of 172.9° in 1 lies in one of the most bent structures. The CSD search also indicated that diazo compounds consisting of a distorted diazo carbon tend to bend the Cdbnd N+dbnd N- bond. On the basis of DFT calculations (B3LYP/6-311++G(d,p)) of model compounds, it was revealed that the bending of the CNN bond is principally induced by steric factors and that the neighboring carbonyl group also plays a role in bending toward the carbonyl side owing to an electrostatic attractive interaction. The potential surface along the path of Cdbnd N+dbnd N- bending in 2-diazopropane shows a significantly shallow profile with only 4 kcal/mol needed to bend the Cdbnd N+dbnd N- bond from 180° to 160°. Thus, the bending of the diazo group in 1 is reasonable as it is provided with all of the factors for facile bending disclosed in this investigation.

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

Directory of Open Access Journals (Sweden)

Giuseppe Scarpa

2018-02-01

Full Text Available Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI, using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

[Development of expert diagnostic system for common respiratory diseases].

Science.gov (United States)

Xu, Wei-hua; Chen, You-ling; Yan, Zheng

2014-03-01

To develop an internet-based expert diagnostic system for common respiratory diseases. SaaS system was used to build architecture; pattern of forward reasoning was applied for inference engine design; ASP.NET with C# from the tool pack of Microsoft Visual Studio 2005 was used for website-interview medical expert system.The database of the system was constructed with Microsoft SQL Server 2005. The developed expert system contained large data memory and high efficient function of data interview and data analysis for diagnosis of various diseases.The users were able to perform this system to obtain diagnosis for common respiratory diseases via internet. The developed expert system may be used for internet-based diagnosis of various respiratory diseases,particularly in telemedicine setting.

Diagnostic system for process control at NPP Dukovany load follow

International Nuclear Information System (INIS)

Rubek, J.; Petruzela, I.

1998-01-01

The NPP Dukovany is being operated in the frequency control since 1996. In last year a project for the plant load follow has been developed. One part of the project is to install a diagnostic system for process control. At present the main control loops of the plant control system are regular tested after unit refuelling only. The functionality and control system parameter adjusting is tested by certificated procedures. This state is unsuitable in view of the plan load follow operation. The relevant operational modes are based on minimisation of influence on plant component life time and on achievement of planned unit parameters. Therefore it is necessary to provide testing of main control system parts in shorter time period. Mainly at time when the unit is really in load follow operation. The paper describes the diagnostic system for process control which will be at NPP Dukovany implemented. The principal of the system will be evaluation of real and expected changes of technological variables. The system utilises thermohydraulic relation among main technological variables and relation among controlled and manipulated variables. Outputs of the system will be used to operational staff support at the plant operation. It enables: determination of control system state, estimation and check of future control system state, early indication of the deviation of process from normal conditions, check of efficiency of operational staff intervention into plant control. The system gives the plant operator new information for the plant process control. Simultaneously the coupling of new system outputs on existing signalisation is solved. (author)

[Primary malignant melanoma of the central nervous system: A diagnostic challenge].

Science.gov (United States)

Quillo-Olvera, Javier; Uribe-Olalde, Juan Salvador; Alcántara-Gómez, Leopoldo Alberto; Rejón-Pérez, Jorge Dax; Palomera-Gómez, Héctor Guillermo

2015-01-01

The rare incidence of primary malignant melanoma of the central nervous system and its ability to mimic other melanocytic tumors on images makes it a diagnostic challenge for the neurosurgeon. A 51-year-old patient, with a tumor located in the right forniceal callosum area. Total surgical excision was performed. Histopathological result was consistent with the diagnosis of primary malignant melanoma of the central nervous system, after ruling out extra cranial and extra spinal melanocytic lesions. The primary malignant melanoma of the central nervous system is extremely rare. There are features in magnetic resonance imaging that increase the diagnostic suspicion; nevertheless there are other tumors with more prevalence that share some of these features through image. Since there is not an established therapeutic standard its prognosis is discouraging. Copyright © 2015 Academia Mexicana de Cirugía A.C. Published by Masson Doyma México S.A. All rights reserved.

Expert system for diagnostics and status monitoring of NPP water chemistry condition

International Nuclear Information System (INIS)

Shvedova, M.N.; Kritski, V.G.; Zakharova, S.V.; Benediktov, V.B.; Nikolaev, F.V.

2002-01-01

Water chemistry condition (WCC) has been the subject of constant study and improvement up to the present day. It is connected with the presence of a direct relationship between the violation of water chemistry regulation on the one hand and components reliability of the circuit's equipment and cost-effectiveness of their operation on the other. It dictates the necessity to apply different optimization methods in the field of monitoring and use of information analytical and diagnostic systems to assess WCC quality, control and support. LI ''VNIPIET'' employees have, for several years, been developing an expert diagnostic system for supporting WCC and status monitoring of RBMK - reactor NPPs [2]. This system has not only conveniently organized the traditional functions of information acquisition and storage, a complete presentation of information in the form of tables, graphs of a dynamical changes of parameters and formation regular reports, diagnostic functions and issuing recommendations on WCC correction, but it also allows the assessment of confidence in the diagnosis made, relying on a wide range of numerical estimates, which were calculated by the use of expert data, and to make a credible prediction of an existing situation development. (authors)

Renal diagnostic nuclear medicine procedures in progressive systemic scleroderma (PSS)

Energy Technology Data Exchange (ETDEWEB)

Ammari, B.; Hotze, A.; Gruenwald, F.; Biersack, H.J.; Blitz, H.; Kuester, W.; Kreysel, H.W.

1989-02-01

The involvement of kidneys in progressive systemic scleroderma (PSS) is one of the most frequent causes of death in this disease. Using clinical criteria and laboratory tests only the frequency of kidney involvement would be clearly underestimated. Invasive diagnostic procedures such as biopsy and angiography can not be applied in those patients. Nuclear medicine techniques (hippurate clearance, DMSA-scan), however, offer non invasive and sensitive methods in the diagnosis of renal involvement in PSS patients. In our study 46 of 76 patients (60%) revealed pathologic findings. The mentioned diagnostic techniques show a high sensitivity and are in agreement with pathological findings described in PSS.

Renal diagnostic nuclear medicine procedures in progressive systemic scleroderma (PSS)

International Nuclear Information System (INIS)

Ammari, B.; Hotze, A.; Gruenwald, F.; Biersack, H.J.; Blitz, H.; Kuester, W.; Kreysel, H.W.

1989-01-01

The involvement of kidneys in progressive systemic scleroderma (PSS) is one of the most frequent causes of death in this disease. Using clinical criteria and laboratory tests only the frequency of kidney involvement would be clearly underestimated. Invasive diagnostic procedures such as biopsy and angiography can not be applied in those patients. Nuclear medicine techniques (hippurate clearance, DMSA-scan), however, offer non invasive and sensitive methods in the diagnosis of renal involvement in PSS patients. In our study 46 of 76 patients (60%) revealed pathologic findings. The mentioned diagnostic techniques show a high sensitivity and are in agreement with pathological findings described in PSS. (orig.) [de

Development of a Diagnostic System for Information Ethics Education

Science.gov (United States)

Shiota, Shingo; Sakai, Kyohei; Kobayashi, Keita

2016-01-01

This paper presents a new diagnostic system for information ethics education. In order to educate children about information ethics, it is necessary to know the stage at which they currently are in terms of their knowledge of the same. Some actual condition surveys have been conducted by the Cabinet Office and the National Police Agency to gauge…

Cable Diagnostic Focused Initiative

Energy Technology Data Exchange (ETDEWEB)

Hartlein, R.A.; Hampton, R.N.

2010-12-30

This report summarizes an extensive effort made to understand how to effectively use the various diagnostic technologies to establish the condition of medium voltage underground cable circuits. These circuits make up an extensive portion of the electric delivery infrastructure in the United States. Much of this infrastructure is old and experiencing unacceptable failure rates. By deploying efficient diagnostic testing programs, electric utilities can replace or repair circuits that are about to fail, providing an optimal approach to improving electric system reliability. This is an intrinsically complex topic. Underground cable systems are not homogeneous. Cable circuits often contain multiple branches with different cable designs and a range of insulation materials. In addition, each insulation material ages differently as a function of time, temperature and operating environment. To complicate matters further, there are a wide variety of diagnostic technologies available for assessing the condition of cable circuits with a diversity of claims about the effectiveness of each approach. As a result, the benefits of deploying cable diagnostic testing programs have been difficult to establish, leading many utilities to avoid the their use altogether. This project was designed to help address these issues. The information provided is the result of a collaborative effort between Georgia Tech NEETRAC staff, Georgia Tech academic faculty, electric utility industry participants, as well as cable system diagnostic testing service providers and test equipment providers. Report topics include: •How cable systems age and fail, •The various technologies available for detecting potential failure sites, •The advantages and disadvantages of different diagnostic technologies, •Different approaches for utilities to employ cable system diagnostics. The primary deliverables of this project are this report, a Cable Diagnostic Handbook (a subset of this report) and an online

Modern technical diagnostic system for the main components of powerful turbine generator

International Nuclear Information System (INIS)

Ezovit, G.P.; Uglyarenko, V.P.; Burlaka, S.I.; Goroz, N.I.; Orinin, S.E.; Komaritsa, V.N.; Zav'yalov, D.N.; Mazurenko, O.A.

2011-01-01

The modern diagnostic system to monitor the technical state of a powerful turbine generator is considered. This system permits the detection of defects in its main components and cooling system at the early stage of their development, prevention of damage and, as a consequence, emergency shutdown of nuclear power units

Diagnostic method for photovoltaic systems based on light I-V measurements

DEFF Research Database (Denmark)

Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

2015-01-01

, be it external, such as shading or soiling, or degradation or failure of the PV modules and balance-of-system components. This allows for performing preventive and/or reparative maintenance, thus minimizing further losses and costs. This article proposes a complete diagnostic method for detecting shading...... and analysis of the diagnostic parameters and logic was performed based on module level tests on standard crystalline silicon PV modules, and were optimized to detect even small partial shading and increase series-resistance losses. To demonstrate the practical application and operation of this method...

Commissioning results of the APS storage ring diagnostics systems

International Nuclear Information System (INIS)

Lumpkin, A.H.

1996-01-01

Initial commissionings of the Advanced Photon Source (APS) 7-GeV storage ring and its diagnostics systems have been done. Early studies involved single-bunch measurements for beam transverse size (σ x ∼ 150 μm, σ y ∼ 50 μm), current, injection losses, and bunch length. The diagnostics have been used in studies related to the detection of an extra contribution to beam jitter at ∼ 6.5 Hz frequency; observation of bunch lengthening (σ ∼ 30 to 60 ps) with single-bunch current; observation of an induced vertical, head-tail instability; and detection of a small orbit change with insertion device gap position. More recently, operations at 100-mA stored-beam current, the baseline design goal, have been achieved with the support of beam characterizations

A diagnostic methodology for refrigerating systems; Methodologie de diagnostic des installations frigorifiques

Energy Technology Data Exchange (ETDEWEB)

Vrinat, G. [Association Francaise du Froid (AFF), 75 - Paris (France)

1997-12-31

A diagnostic methodology for refrigerating machines, equipment and plants has been defined and evaluated for EDF, the French national power utility and ADEME, the French Agency for Energy Conservation, in the framework of energy conservation objectives: the diagnostic method should enable to identify malfunctions, assess the cost efficiency of the equipment, identify limiting factors, and consider corrective measures

The Types of Argument Structure Used by Hillary Clinton in the CNN Democratic Presidential Debate

Directory of Open Access Journals (Sweden)

Anggie Angeline

2009-01-01

Full Text Available This qualitative research was conducted to examine the types of argument structure by Hillary Clinton in part one of the CNN Democratic Presidential Debate since Hillary, who had a great deal of experiences in political parties, was supposed to be able to construct convincing arguments that had good argument structures. The theories used to analyze were Bierman and Assali’s (1996, King’s (n.d. and Stanlick’s (2003. The findings showed that there were five types of argument structure used: serial, linked, convergent, divergent, and hybrid argument structures. The linked argument structure was the argument structure used the most frequently in Hillary’s utterances in the debate, while the divergent was the least one. Thus, it could be concluded that Hillary’s speech in the Presidential Debate was quite interesting since she could combine all the five types of argument structure, though the frequency of using them was not the same and it seems that linked argument structure was the most effective strategy for her in arguing about the politic, economy, and social issues.

The application of artificial intelligence chemistry diagnostic system to nuclear power plants

International Nuclear Information System (INIS)

Chen Meizhen

1996-01-01

By processing water chemistry data to diagnose sensor and equipment malfunctions in realtime, artificial intelligence chemistry diagnostic system helps to reduce the plant downtime due to steam generator tubing failures and other accidents. A typical processing system of water chemistry data is presented

Diagnostic accuracy of commercial system for computer-assisted detection (CADx) as an adjunct to interpretation of mammograms

International Nuclear Information System (INIS)

Menna, Sabatino; Di Virgilio, Maria Rosaria; Burke, Paolo; Frigerio, Alfonso; Boglione, Elisa; Ciccarelli, Grazia; Di Filippo, Sabato; Garretti, Licia

2005-01-01

Purpose. To evaluate the diagnostic accuracy of the commercial computer-aided detection CADx system for the reading of mammograms. Materials and methods. The study assessed the Second Look system developed and marketed by CADx Medical Systems, Montreal, Canada. The diagnostic sensitivity was evaluated by means of a retrospective study on 98 consecutive cancers detected at screening by double independent reading. The specificity and the positive predictive value (PPV) for cancer of the CADx system were prospectively evaluated on a second group of 560 consecutive mammograms of asymptomatic women not included in screening program. The radiologist who was present during the test assessed the abnormal mammographic findings by one or more of the following diagnostic procedures: physical examination, additional mammographic detail views with or without magnification,ultrasonography, ultrasound- or mammography guided fine needle aspiration cytology, and core-biopsy. The exams first underwent conventional reading and then a second reading carried out with the aid of the CADx system. Results.The overall diagnostic sensitivity of the CADx system on the 98 screening cancers was 81.6%; in particular it was 89.3% for calcifications, 83.9% for masses and only 37.5% for architectural distortion. The CADx markings for each mammography were 4.7 on average. Identification of invasive carcinoma was independent from tumour size. In the second group of 560 mammograms, the CADx system marked all cases identified as positive by conventional reading and confirmed by biopsy (7/7), but did not permit the detection of any additional cancer. The CADx markings per exam were 4.2 on average, the specificity was 13.7% and the PPV was 0.55% versus 13.7% recall rate of conventional reading. CADx reading led to a 1.96% (11/560) increase of the women necessitating further diagnostic investigation. Conclusions. The results of our study show that the diagnostic sensitivity of the CADx system is lower

Reactor protection system with automatic self-testing and diagnostic

International Nuclear Information System (INIS)

Gaubatz, D.C.

1996-01-01

A reactor protection system is disclosed having four divisions, with quad redundant sensors for each scram parameter providing input to four independent microprocessor-based electronic chassis. Each electronic chassis acquires the scram parameter data from its own sensor, digitizes the information, and then transmits the sensor reading to the other three electronic chassis via optical fibers. To increase system availability and reduce false scrams, the reactor protection system employs two levels of voting on a need for reactor scram. The electronic chassis perform software divisional data processing, vote 2/3 with spare based upon information from all four sensors, and send the divisional scram signals to the hardware logic panel, which performs a 2/4 division vote on whether or not to initiate a reactor scram. Each chassis makes a divisional scram decision based on data from all sensors. Automatic detection and discrimination against failed sensors allows the reactor protection system to automatically enter a known state when sensor failures occur. Cross communication of sensor readings allows comparison of four theoretically ''identical'' values. This permits identification of sensor errors such as drift or malfunction. A diagnostic request for service is issued for errant sensor data. Automated self test and diagnostic monitoring, sensor input through output relay logic, virtually eliminate the need for manual surveillance testing. This provides an ability for each division to cross-check all divisions and to sense failures of the hardware logic. 16 figs

Final results from the development of the diagnostic expert system DESYRE

International Nuclear Information System (INIS)

Scherer, K.P.; Eggert, H.; Sheleisiek, K.; Stille, P.; Schoeller, H.

1997-01-01

In the Kernforschungszentrum Karlsruhe (KfK), a distributed knowledge based diagnostic system is developed to supervise the primary system including the core of the Kompakte Natriumgekuehlte Kernreaktoranlage (KNK II), a 20 MWe experimental fast reactor. The problem is to detect anomalies and disturbances in the beginning state before fault propagation - early diagnosis - and provide the scram analysis to detect the causality when a system shutdwon occurs. (author). 9 refs, 15 figs

Video profile monitor diagnostic system for GTA

International Nuclear Information System (INIS)

Sandovil, D.P.; Garcia, R.C.; Gilpatrick, J.D.; Johnson, K.F.; Shinas, M.A.; Wright, R.; Yuan, V.; Zander, M.E.

1992-01-01

This paper describes a video diagnostic system used to measure the beam profile and position of the Ground Test Accelerator 2.5 MeV H - ion beam as it exits the intermediate matching section. Inelastic collisions between H - ions and residual nitrogen in the vacuum chamber cause the nitrogen to fluoresce. The resulting light is captured through transport optics by an intensified CCD camera and is digitized. Real-time beam profile images are displayed and stored for detailed analysis. Analyzed data showing resolutions for both position and profile measurements will also be presented. (Author) 5 refs., 7 figs

Dome diagnostics system of optical parameters and characteristics of LEDs

Science.gov (United States)

Peretyagin, Vladimir S.; Pavlenko, Nikita A.

2017-09-01

Scientific and technological progress of recent years in the production of the light emitting diodes (LEDs) has led to the expansion of areas of their application from the simplest systems to high precision lighting devices used in various fields of human activity. However, development and production (especially mass production) of LED lighting devices are impossible without a thorough analysis of its parameters and characteristics. There are many ways and devices for analysis the spatial, energy and colorimetric parameters of LEDs. The most methods are intended for definition only one parameter (for example, luminous flux) or one characteristic (for example, the angular distribution of energy or the spectral characteristics). Besides, devices used these methods are intended for measuring parameters in only one point or plane. This problem can be solved by using a dome diagnostics system of optical parameters and characteristics of LEDs, developed by specialists of the department OEDS chair of ITMO University in Russia. The paper presents the theoretical aspects of the analysis of LED's spatial (angular), energy and color parameters by using mentioned of diagnostics system. The article also presents the results of spatial), energy and color parameters measurements of some LEDs brands.

Modeling and Measurement Constraints in Fault Diagnostics for HVAC Systems

Energy Technology Data Exchange (ETDEWEB)

Najafi, Massieh; Auslander, David M.; Bartlett, Peter L.; Haves, Philip; Sohn, Michael D.

2010-05-30

Many studies have shown that energy savings of five to fifteen percent are achievable in commercial buildings by detecting and correcting building faults, and optimizing building control systems. However, in spite of good progress in developing tools for determining HVAC diagnostics, methods to detect faults in HVAC systems are still generally undeveloped. Most approaches use numerical filtering or parameter estimation methods to compare data from energy meters and building sensors to predictions from mathematical or statistical models. They are effective when models are relatively accurate and data contain few errors. In this paper, we address the case where models are imperfect and data are variable, uncertain, and can contain error. We apply a Bayesian updating approach that is systematic in managing and accounting for most forms of model and data errors. The proposed method uses both knowledge of first principle modeling and empirical results to analyze the system performance within the boundaries defined by practical constraints. We demonstrate the approach by detecting faults in commercial building air handling units. We find that the limitations that exist in air handling unit diagnostics due to practical constraints can generally be effectively addressed through the proposed approach.

Comparison of serum amyloid A and C-reactive protein as diagnostic markers of systemic inflammation in dogs

DEFF Research Database (Denmark)

Christensen, Michelle Brønniche; Langhorn, Rebecca; Goddard, Amelia

2014-01-01

The diagnostic performance of canine serum amyloid A (SAA) was compared with that of C-reactive protein (CRP) in the detection of systemic inflammation in dogs. Sera from 500 dogs were retrospectively included in the study. C-reactive protein and SAA were measured using validated automated assays....... The overlap performance, clinical decision limits, overall diagnostic performance, correlations, and agreement in the clinical classification between these 2 diagnostic markers were compared. Significantly higher concentrations of both proteins were detected in dogs with systemic inflammation (SAA range: 48.......75 to > 2700 mg/L; CRP range: 0.4 to 907.4 mg/L) compared to dogs without systemic inflammation (SAA range: 1.06 to 56.4 mg/L; CRP range: 0.07 to 24.7 mg/L). Both proteins were shown to be sensitive and specific markers of systemic inflammation in dogs. Significant correlations and excellent diagnostic...

Diagnostic-management system and test pulse acquisition for WEST plasma measurement system

International Nuclear Information System (INIS)

Wojenski, A.; Kasprowicz, G.; Pozniak, K.T.; Byszuk, A.; Juszczyk, B.; Zabolotny, W.; Zienkiewicz, P.; Chernyshova, M.; Czarski, T.; Mazon, D.; Malard, P.

2014-01-01

This paper describes current status of electronic, firmware and software development for new plasma measurement system for use in WEST facility. The system allows to perform two dimensional plasma visualization (in time) with spectrum measurement. The analog front-end is connected to Gas Electron Multiplier detector (GEM detector). The system architecture have high data throughput due to use of PCI-Express interface, Gigabit Transceivers and sampling frequency of ADC integrated circuits. The hardware is based on several years of experience in building X-ray spectrometer system for Joint European Torus (JET) facility. Data streaming is done using Artix7 FPGA devices. The system in basic configuration can work with up to 256 channels, while the maximum number of measurement channels is 2048. Advanced firmware for the FPGA is required in order to perform high speed data streaming and analog signal sampling. Diagnostic system management has been developed in order to configure measurement system, perform necessary calibration and prepare hardware for data acquisition. (authors)

Commitments of Psychological Contracts and Diagnostic Use of Management Control Systems

Directory of Open Access Journals (Sweden)

Ivan Canan

2016-06-01

Full Text Available Investigating the commitments the Surveillance Agents from the National Telecommunications Agency (Anatel made in their psychological contracts and the diagnostic use of the management control system of the entity, this study tested the hypothesis that individuals tend to be more committed to aspects they are charged for within organizations. This is a theoretical and empirical study that assumed that the commitments comprise the part of the belief that individuals develop on reciprocal relations of exchange between themselves and their contractors, in line with the model by Rousseau (1989; 1995. It was also assumed that the extent to which aspects are charged from the members of an organization matches their perception of the diagnostic use of formal and informal control systems. Methodologically, the research was developed in two phases, the first qualitative, involving documentary analysis and content analysis of organizational documents; and the second quantitative, with the application of questionnaires answered by individuals who occupy the individual position referred to in the organization, who evaluated behavioral parameters that act on them and that were identified in the first phase. The data showed that 42 respondents tend to maintain high levels of commitment to the rules and standards proposed for their function. Statistical results also suggest that there is a significant positive correlation between the commitments assumed and the perceived diagnostic use of control systems for the surveillance agents who answered the questionnaire.

TFTR diagnostic vacuum controller

International Nuclear Information System (INIS)

Olsen, D.; Persons, R.

1981-01-01

The TFTR diagnostic vacuum controller (DVC) provides in conjunction with the Central Instrumentation Control and Data Acquisition System (CICADA), control and monitoring for the pumps, valves and gauges associated with each individual diagnostic vacuum system. There will be approximately 50 systems on TFTR. Two standard versions of the controller (A and B) wil be provided in order to meet the requirements of two diagnostic manifold arrangements. All pump and valve sequencing, as well as protection features, will be implemented by the controller

Pathohistological classification systems in gastric cancer: diagnostic relevance and prognostic value.

Science.gov (United States)

Berlth, Felix; Bollschweiler, Elfriede; Drebber, Uta; Hoelscher, Arnulf H; Moenig, Stefan

2014-05-21

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer.

EULAR points to consider in the development of classification and diagnostic criteria in systemic vasculitis

DEFF Research Database (Denmark)

Basu, Neil; Watts, Richard; Bajema, Ingeborg

2010-01-01

The systemic vasculitides are multiorgan diseases where early diagnosis and treatment can significantly improve outcomes. Robust nomenclature reduces diagnostic delay. However, key aspects of current nomenclature are widely perceived to be out of date, these include disease definitions, classific......, classification and diagnostic criteria. Therefore, the aim of the present work was to identify deficiencies and provide contemporary points to consider for the development of future definitions and criteria in systemic vasculitis....

Leishmania Surveillance and Diagnostic Capability in Support of the Joint Biological Agent Identification and Diagnostic System (JBAIDS) and Leishmania Vector Surveillance

Science.gov (United States)

2013-02-07

01-10-09 to 07-02-13 ’+. I II L~ J.\\NU :::OU~ Ill L~ :la. l-UI’I I 11J.\\l- I NUIVI~~I1 LEISHMANIA SURVEILLANCE AND DIAGNOSTIC CAPABILITY IN None...SUPPORT OF THE JOINT BIOLOGICAL AGENT IDENTIFICATION AND :lD. l:JI1J.\\NI NUIVI~~I1 DIAGNOSTIC SYSTEM (JBAIDS) None . ./ LEISHMANIA VECTOR...Field Station at Kisumu completed project activities through a resource sharing arrangement with the 59th MDW. Testing of the Leishmania epidemiology

Implementing artificial neural networks in nuclear power plants diagnostic systems: issues and challenges

International Nuclear Information System (INIS)

Boger, Z.

1998-01-01

A recent review of artificial intelligence applications in nuclear power plants (NPP) diagnostics and fault detection finds that mostly expert systems (ES) and artificial neural networks (ANN) techniques were researched and proposed, but the number of actual implementations in NPP diagnostics systems is very small. It lists the perceived obstacles to the ANN-based system acceptance and implementation. This paper analyses this list. Some of ANN limitations relate to 'quantitative' difficulties of designing and training large-scale ANNs. The availability of an efficient large-scale ANN training algorithm may alleviate most of these concerns. Other perceived drawbacks refer to the 'qualitative' aspects of ANN acceptance - how and when can we rely on the quality of the advice given by the ANN model. Several techniques are available that help to brighten the 'black box' image of the ANN. Analysis of the trained ANN can identify the significant inputs. Calculation of the Causal Indices may reveal the magnitude and sign of the influence of each input on each output. Both these techniques increase the confidence of the users when they conform to known knowledge, or point to plausible relationships. Analysis of the behavior of the neurons in the hidden layer can identify false ANN classification when presented with noisy or corrupt data. Auto-associative NN can identify faulty sensors or data. Two examples of the ANN capabilities as possible diagnostic tools are given, using NPP data, one classifying internal reactor disturbances by neutron noise spectra analysis, the other identifying the faults causes of several transients. To use these techniques the ANN developers need large amount of training data of as many transients as possible. Such data is routinely generated in NPP simulators during the periodic qualification of NPP operators. The IAEA can help by encouraging the saving and distributing the transient data to developers of ANN diagnostic system, to serve as

Telecontrol - Expert systems. Real-time monitoring and remote diagnostic

Energy Technology Data Exchange (ETDEWEB)

Lam, A.

1996-09-01

The role of expert systems in programming simple and complex tasks in utilities companies was discussed with examples from B.C. Hydro, where expert systems have been used in such diverse applications as an in-house programmable logic controller (PLC) training course, and a machine audit on a 150 MW steam turbine generating unit at their Burrard Thermal Generating Plant. The PLC tutoring program uses expert system technology for the air blast circuit breakers` air drier system, for individualized on-site training. The steam turbine audits (an eight-month long project) were performed remotely by dialing an on-site computer configured with customized expert software. Details of these, and other potential applications, such as transformer monitoring and diagnostics, circuit breaker performance analysis, and information management, were described.

Systemic lupus erythematosus diagnostics in the ‘omics’ era

Science.gov (United States)

Arriens, Cristina; Mohan, Chandra

2014-01-01

Systemic lupus erythematosus is a complex autoimmune disease affecting multiple organ systems. Currently, diagnosis relies upon meeting at least four out of eleven criteria outlined by the ACR. The scientific community actively pursues discovery of novel diagnostics in the hope of better identifying susceptible individuals in early stages of disease. Comprehensive studies have been conducted at multiple biological levels including: DNA (or genomics), mRNA (or transcriptomics), protein (or proteomics) and metabolites (or metabolomics). The ‘omics’ platforms allow us to re-examine systemic lupus erythematosus at a greater degree of molecular resolution. More importantly, one is hopeful that these ‘omics’ platforms may yield newer biomarkers for systemic lupus erythematosus that can help clinicians track the disease course with greater sensitivity and specificity. PMID:24860621

Studying the Impact of Spaceflight Environment on Immune Functions Using New Molecular Diagnostics System

Science.gov (United States)

Cohen, Luchino

Immune functions are altered during space flights. Latent virus reactivation, reduction in the number of immune cells, decreased cell activation and increased sensitivity of astronauts to infections following their return on Earth demonstrate that the immune system is less efficient during space flight. The causes of this immune deficiency are not fully understood and this dysfunction during long-term missions could result in the appearance of opportunistic infections or a decrease in the immuno-surveillance mechanisms that eradicate cancer cells. Therefore, the immune functions of astronauts will have to be monitored continuously during long-term missions in space, using miniature and semi-automated diagnostic systems. The objectives of this project are to study the causes of space-related immunodeficiency, to develop countermeasures to maintain an optimal immune function and to improve our capacity to detect infectious diseases during space missions through the monitoring of astronauts' immune system. In order to achieve these objectives, an Immune Function Diagnostic System (IFDS) will be designed to perform a set of immunological assays on board spacecrafts or on planet-bound bases. Through flow cytometric assays and molecular biology analyses, this diagnostic system could improve medical surveillance of astronauts and could be used to test countermeasures aimed at preventing immune deficiency during space missions. The capacity of the instrument to assess cellular fluorescence and to quantify the presence of soluble molecules in biological samples would support advanced molecular studies in space life sciences. Finally, such diagnostic system could also be used on Earth in remote areas or in mobile hospitals following natural disasters to fight against infectious diseases and other pathologies.

Diagnostics system for the 67 MJ, 50 kV pulsed power capacitor bank

Energy Technology Data Exchange (ETDEWEB)

Galakhov, I V; Gasheev, A S; Gruzin, I A; Gudov, S N; Murugov, V M; Osin, V A; Pankratov, V I; Pegoev, I N [All-Russian Scientific Research Inst. of Experimental Physics, Sarov (Russian Federation)

1997-12-31

The diagnostics system is designed for charging and discharging to the load of the large 67 MJ and 50 kV capacitor bank for the iodine laser pulse power of ISKRA-5 facility. Discharging diagnostics of the capacitor bank uses a technique to measure a sequence of times between representative discharge events for 665 discharge circuits of the bank. Benefits of the measurement techniques are discussed. (author). 3 figs., 3 refs.

Diagnostics system for the 67 MJ, 50 kV pulsed power capacitor bank

International Nuclear Information System (INIS)

Galakhov, I.V.; Gasheev, A.S.; Gruzin, I.A.; Gudov, S.N.; Murugov, V.M.; Osin, V.A.; Pankratov, V.I.; Pegoev, I.N.

1996-01-01

The diagnostics system is designed for charging and discharging to the load of the large 67 MJ and 50 kV capacitor bank for the iodine laser pulse power of ISKRA-5 facility. Discharging diagnostics of the capacitor bank uses a technique to measure a sequence of times between representative discharge events for 665 discharge circuits of the bank. Benefits of the measurement techniques are discussed. (author). 3 figs., 3 refs

Ion beam probe plasma diagnostic system. Technical progress report, 1 February 1977--31 December 1978

International Nuclear Information System (INIS)

Hickok, R.L.; Jennings, W.C.; Woo, J.T.; connor, K.A.

1979-01-01

During this time, reliable operation of the research tokamak, RENTOR, has been established but thedischarge is characterized by a large runaway populaion producing a strong x-ray flux. An ion beam probe and a Thomson scattering diagnostic system have been installed on RENTOR, but no definitive results have been obtained due to the large noise signal generated by the x-ray flux. The ALICE baseball coil has been obtained on loan from LLL and is being set up as the ALEX mirror system. It will be used for particle beam diagnostic development in 3 dimensional magnetic well geometry. A heavy neutral beam diagnostic system has been designed and is under construction for measuring the space potential in ALEX. Improvements in the focusing properties of ion guns and in the sensitivity of the feedback controlled electrostatic energy analyzers have been obtained

Design of a magnetic field alignment diagnostic for the MFTF-B magnet system

International Nuclear Information System (INIS)

Deadrick, F.J.; House, P.A.; Frye, R.W.

1985-01-01

Magnet alignment in tandem mirror fusion machines plays a crucial role in achieving and maintaining plasma confinement. Various visual alignment tools have been described by Post et al. to align the Tara magnet system. We have designed and installed a remotely operated magnetic field alignment (MFA) diagnostic system as a part of the Mirror Fusion Test Facility (MFTF-B). It measures critical magnetic field alignment parameters of the MFTF-B coil set while under full-field operating conditions. The MFA diagnostic employs a pair of low-energy, electron beam guns on a remotely positionable probe to trace and map selected magnetic field lines. An array of precision electrical detector paddles locates the position of the electron beam, and thus the magnetic field line, at several critical points. The measurements provide a means to compute proper compensating currents to correct for mechanical misalignments of the magnets with auxiliary trim coils if necessary. This paper describes both the mechanical and electrical design of the MFA diagnostic hardware