Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

PubMed Central

Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

2017-01-01

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. PMID:29095934

Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

NASA Astrophysics Data System (ADS)

Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

2015-12-01

In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

A Rapid Identification Method for Calamine Using Near-Infrared Spectroscopy Based on Multi-Reference Correlation Coefficient Method and Back Propagation Artificial Neural Network.

PubMed

Sun, Yangbo; Chen, Long; Huang, Bisheng; Chen, Keli

2017-07-01

As a mineral, the traditional Chinese medicine calamine has a similar shape to many other minerals. Investigations of commercially available calamine samples have shown that there are many fake and inferior calamine goods sold on the market. The conventional identification method for calamine is complicated, therefore as a result of the large scale of calamine samples, a rapid identification method is needed. To establish a qualitative model using near-infrared (NIR) spectroscopy for rapid identification of various calamine samples, large quantities of calamine samples including crude products, counterfeits and processed products were collected and correctly identified using the physicochemical and powder X-ray diffraction method. The NIR spectroscopy method was used to analyze these samples by combining the multi-reference correlation coefficient (MRCC) method and the error back propagation artificial neural network algorithm (BP-ANN), so as to realize the qualitative identification of calamine samples. The accuracy rate of the model based on NIR and MRCC methods was 85%; in addition, the model, which took comprehensive multiple factors into consideration, can be used to identify crude calamine products, its counterfeits and processed products. Furthermore, by in-putting the correlation coefficients of multiple references as the spectral feature data of samples into BP-ANN, a BP-ANN model of qualitative identification was established, of which the accuracy rate was increased to 95%. The MRCC method can be used as a NIR-based method in the process of BP-ANN modeling.

Research on wind field algorithm of wind lidar based on BP neural network and grey prediction

NASA Astrophysics Data System (ADS)

Chen, Yong; Chen, Chun-Li; Luo, Xiong; Zhang, Yan; Yang, Ze-hou; Zhou, Jie; Shi, Xiao-ding; Wang, Lei

2018-01-01

This paper uses the BP neural network and grey algorithm to forecast and study radar wind field. In order to reduce the residual error in the wind field prediction which uses BP neural network and grey algorithm, calculating the minimum value of residual error function, adopting the residuals of the gray algorithm trained by BP neural network, using the trained network model to forecast the residual sequence, using the predicted residual error sequence to modify the forecast sequence of the grey algorithm. The test data show that using the grey algorithm modified by BP neural network can effectively reduce the residual value and improve the prediction precision.

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

PubMed

Huang, Yin-Fu; Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

PubMed Central

Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

2017-01-01

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

PubMed Central

Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. PMID:23737711

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

Usage of the back-propagation method for alphabet recognition

NASA Astrophysics Data System (ADS)

Shaila Sree, R. N.; Eswaran, Kumar; Sundararajan, N.

1999-03-01

Artificial Neural Networks play a pivotal role in the branch of Artificial Intelligence. They can be trained efficiently for a variety of tasks using different methods, of which the Back Propagation method is one among them. The paper studies the choosing of various design parameters of a neural network for the Back Propagation method. The study shows that when these parameters are properly assigned, the training task of the net is greatly simplified. The character recognition problem has been chosen as a test case for this study. A sample space of different handwritten characters of the English alphabet was gathered. A Neural net is finally designed taking many the design aspects into consideration and trained for different styles of writing. Experimental results are reported and discussed. It has been found that an appropriate choice of the design parameters of the neural net for the Back Propagation method reduces the training time and improves the performance of the net.

On-line dynamic monitoring automotive exhausts: using BP-ANN for distinguishing multi-components

NASA Astrophysics Data System (ADS)

Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

2017-10-01

Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3 5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2 and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts.

Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

2016-10-01

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.

PubMed

Kim, Keo-Sik; Seo, Jeong-Hwan; Song, Chul-Gyu

2011-08-10

Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised. Twelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN. As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor

NASA Astrophysics Data System (ADS)

Fu, Liyue; Song, Aiguo

2018-02-01

In order to improve the measurement precision of 6-axis force/torque sensor for robot, BP decoupling algorithm optimized by GA (GA-BP algorithm) is proposed in this paper. The weights and thresholds of a BP neural network with 6-10-6 topology are optimized by GA to develop decouple a six-axis force/torque sensor. By comparison with other traditional decoupling algorithm, calculating the pseudo-inverse matrix of calibration and classical BP algorithm, the decoupling results validate the good decoupling performance of GA-BP algorithm and the coupling errors are reduced.

Research on particle swarm optimization algorithm based on optimal movement probability

NASA Astrophysics Data System (ADS)

Ma, Jianhong; Zhang, Han; He, Baofeng

2017-01-01

The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.

An improved wavelet neural network medical image segmentation algorithm with combined maximum entropy

NASA Astrophysics Data System (ADS)

Hu, Xiaoqian; Tao, Jinxu; Ye, Zhongfu; Qiu, Bensheng; Xu, Jinzhang

2018-05-01

In order to solve the problem of medical image segmentation, a wavelet neural network medical image segmentation algorithm based on combined maximum entropy criterion is proposed. Firstly, we use bee colony algorithm to optimize the network parameters of wavelet neural network, get the parameters of network structure, initial weights and threshold values, and so on, we can quickly converge to higher precision when training, and avoid to falling into relative extremum; then the optimal number of iterations is obtained by calculating the maximum entropy of the segmented image, so as to achieve the automatic and accurate segmentation effect. Medical image segmentation experiments show that the proposed algorithm can reduce sample training time effectively and improve convergence precision, and segmentation effect is more accurate and effective than traditional BP neural network (back propagation neural network : a multilayer feed forward neural network which trained according to the error backward propagation algorithm.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

PubMed

Liu, Ruixin; Zhang, Xiaodong; Zhang, Lu; Gao, Xiaojie; Li, Huiling; Shi, Junhan; Li, Xuelin

2014-06-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

PubMed Central

LIU, RUIXIN; ZHANG, XIAODONG; ZHANG, LU; GAO, XIAOJIE; LI, HUILING; SHI, JUNHAN; LI, XUELIN

2014-01-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue. PMID:24926369

Using L-M BP Algorithm Forecase the 305 Days Production of First-Breed Dairy

NASA Astrophysics Data System (ADS)

Wei, Xiaoli; Qi, Guoqiang; Shen, Weizheng; Jian, Sun

Aiming at the shortage of conventional BP algorithm, a BP neural net works improved by L-M algorithm is put forward. On the basis of the network, a Prediction model for 305 day's milk productions was set up. Traditional methods finish these data must spend at least 305 days, But this model can forecast first-breed dairy's 305 days milk production ahead of 215 days. The validity of the improved BP neural network predictive model was validated through the experiments.

Determination of zinc oxide content of mineral medicine calamine using near-infrared spectroscopy based on MIV and BP-ANN algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Chen, Long; Sun, Yangbo; Bai, Yu; Huang, Bisheng; Chen, Keli

2018-03-01

Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r2) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4 cm- 1), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9 cm- 1). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of

Development of LC-MS determination method and back-propagation ANN pharmacokinetic model of corynoxeine in rat.

PubMed

Ma, Jianshe; Cai, Jinzhang; Lin, Guanyang; Chen, Huilin; Wang, Xianqin; Wang, Xianchuan; Hu, Lufeng

2014-05-15

Corynoxeine(CX), isolated from the extract of Uncaria rhynchophylla, is a useful and prospective compound in the prevention and treatment for vascular diseases. A simple and selective liquid chromatography mass spectrometry (LC-MS) method was developed to determine the concentration of CX in rat plasma. The chromatographic separation was achieved on a Zorbax SB-C18 (2.1 mm × 150 mm, 5 μm) column with acetonitrile-0.1% formic acid in water as mobile phase. Selective ion monitoring (SIM) mode was used for quantification using target ions m/z 383 for CX and m/z 237 for the carbamazepine (IS). After the LC-MS method was validated, it was applied to a back-propagation artificial neural network (BP-ANN) pharmacokinetic model study of CX in rats. The results showed that after intravenous administration of CX, it was mainly distributed in blood and eliminated quickly, t1/2 was less than 1h. The predicted concentrations generated by BP-ANN model had a high correlation coefficient (R>0.99) with experimental values. The developed BP-ANN pharmacokinetic model can be used to predict the concentration of CX in rats. Copyright © 2014 Elsevier B.V. All rights reserved.

A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

PubMed

Azami, Hamed; Escudero, Javier

2015-08-01

Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

Detection of Foreign Matter in Transfusion Solution Based on Gaussian Background Modeling and an Optimized BP Neural Network

PubMed Central

Zhou, Fuqiang; Su, Zhen; Chai, Xinghua; Chen, Lipeng

2014-01-01

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory. PMID:25347581

Fixing convergence of Gaussian belief propagation

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

Johnson, Jason K; Bickson, Danny; Dolev, Danny

Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper we develop a double-loop algorithm for forcing convergence of GaBP. Our method computes the correct MAP estimate even in cases where standard GaBP would not have converged. We further extend this construction to compute least-squares solutions of over-constrained linear systems. We believe that our construction has numerous applications, since the GaBP algorithm ismore » linked to solution of linear systems of equations, which is a fundamental problem in computer science and engineering. As a case study, we discuss the linear detection problem. We show that using our new construction, we are able to force convergence of Montanari's linear detection algorithm, in cases where it would originally fail. As a consequence, we are able to increase significantly the number of users that can transmit concurrently.« less

Theoretic derivation of directed acyclic subgraph algorithm and comparisons with message passing algorithm

NASA Astrophysics Data System (ADS)

Ha, Jeongmok; Jeong, Hong

2016-07-01

This study investigates the directed acyclic subgraph (DAS) algorithm, which is used to solve discrete labeling problems much more rapidly than other Markov-random-field-based inference methods but at a competitive accuracy. However, the mechanism by which the DAS algorithm simultaneously achieves competitive accuracy and fast execution speed, has not been elucidated by a theoretical derivation. We analyze the DAS algorithm by comparing it with a message passing algorithm. Graphical models, inference methods, and energy-minimization frameworks are compared between DAS and message passing algorithms. Moreover, the performances of DAS and other message passing methods [sum-product belief propagation (BP), max-product BP, and tree-reweighted message passing] are experimentally compared.

Gram-Schmidt algorithms for covariance propagation

NASA Technical Reports Server (NTRS)

Thornton, C. L.; Bierman, G. J.

1977-01-01

This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UD(transpose of U), where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and coloured process noise parameters increase mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.

Gram-Schmidt algorithms for covariance propagation

NASA Technical Reports Server (NTRS)

Thornton, C. L.; Bierman, G. J.

1975-01-01

This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UDU/T/, where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and colored process noise parameters increases mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.

Adaptive laser link reconfiguration using constraint propagation

NASA Technical Reports Server (NTRS)

Crone, M. S.; Julich, P. M.; Cook, L. M.

1993-01-01

This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

PubMed

Zhao, Xin; Han, Meng; Ding, Lili; Calin, Adrian Cantemir

2018-01-01

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society.

Back propagation artificial neural network for community Alzheimer's disease screening in China.

PubMed

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-25

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

Back propagation artificial neural network for community Alzheimer's disease screening in China★

PubMed Central

Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

2013-01-01

Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

Belief Propagation Algorithm for Portfolio Optimization Problems

PubMed Central

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm. PMID:26305462

Belief Propagation Algorithm for Portfolio Optimization Problems.

PubMed

Shinzato, Takashi; Yasuda, Muneki

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.

Back-Propagation Operation for Analog Neural Network Hardware with Synapse Components Having Hysteresis Characteristics

PubMed Central

Ueda, Michihito; Nishitani, Yu; Kaneko, Yukihiro; Omote, Atsushi

2014-01-01

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware. PMID:25393715

Non-invasive prediction of hemoglobin levels by principal component and back propagation artificial neural network

PubMed Central

Ding, Haiquan; Lu, Qipeng; Gao, Hongzhi; Peng, Zhongqi

2014-01-01

To facilitate non-invasive diagnosis of anemia, specific equipment was developed, and non-invasive hemoglobin (HB) detection method based on back propagation artificial neural network (BP-ANN) was studied. In this paper, we combined a broadband light source composed of 9 LEDs with grating spectrograph and Si photodiode array, and then developed a high-performance spectrophotometric system. By using this equipment, fingertip spectra of 109 volunteers were measured. In order to deduct the interference of redundant data, principal component analysis (PCA) was applied to reduce the dimensionality of collected spectra. Then the principal components of the spectra were taken as input of BP-ANN model. On this basis we obtained the optimal network structure, in which node numbers of input layer, hidden layer, and output layer was 9, 11, and 1. Calibration and correction sample sets were used for analyzing the accuracy of non-invasive hemoglobin measurement, and prediction sample set was used for testing the adaptability of the model. The correlation coefficient of network model established by this method is 0.94, standard error of calibration, correction, and prediction are 11.29g/L, 11.47g/L, and 11.01g/L respectively. The result proves that there exist good correlations between spectra of three sample sets and actual hemoglobin level, and the model has a good robustness. It is indicated that the developed spectrophotometric system has potential for the non-invasive detection of HB levels with the method of BP-ANN combined with PCA. PMID:24761296

An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

NASA Astrophysics Data System (ADS)

McLean, N. M.; Bowring, J. F.; Bowring, S. A.

2011-06-01

High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of calculated U-Pb dates. As analytical techniques have advanced, formerly small sources of uncertainty are increasingly important, and thus previous simplifications for data reduction and uncertainty propagation are no longer valid. Although notable previous efforts have treated propagation of correlated uncertainties for the U-Pb system, the equations, uncertainties, and correlations have been limited in number and subject to simplification during propagation through intermediary calculations. We derive and present a transparent U-Pb data reduction algorithm that transforms raw isotopic data and measured or assumed laboratory parameters into the isotopic ratios and dates geochronologists interpret without making assumptions about the relative size of sample components. To propagate uncertainties and their correlations, we describe, in detail, a linear algebraic algorithm that incorporates all input uncertainties and correlations without limiting or simplifying covariance terms to propagate them though intermediate calculations. Finally, a weighted mean algorithm is presented that utilizes matrix elements from the uncertainty propagation algorithm to propagate random and systematic uncertainties for data comparison between other U-Pb labs and other geochronometers. The linear uncertainty propagation algorithms are verified with Monte Carlo simulations of several typical analyses. We propose that our algorithms be considered by the community for implementation to improve the collaborative science envisioned by the EARTHTIME initiative.

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

The application of immune genetic algorithm in main steam temperature of PID control of BP network

NASA Astrophysics Data System (ADS)

Li, Han; Zhen-yu, Zhang

In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.

Optical back propagation for fiber optic networks with hybrid EDFA Raman amplification.

PubMed

Liang, Xiaojun; Kumar, Shiva

2017-03-06

We have investigated an optical back propagation (OBP) method to compensate for propagation impairments in fiber optic networks with lumped Erbium doped fiber amplifier (EDFA) and/or distributed Raman amplification. An OBP module consists of an optical phase conjugator (OPC), optical amplifiers and dispersion varying fibers (DVFs). We derived a semi-analytical expression that calculates the dispersion profile of DVF. The OBP module acts as a nonlinear filter that fully compensates for the nonlinear distortions due to signal propagation in a transmission fiber, and is applicable for fiber optic networks with reconfigurable optical add-drop multiplexers (ROADMs). We studied a wavelength division multiplexing (WDM) network with 3000 km transmission distance and 64-quadrature amplitude modulation (QAM) modulation. OBP brings 5.8 dB, 5.9 dB and 6.1 dB Q-factor gains over linear compensation for systems with full EDFA amplification, hybrid EDFA/Raman amplification, and full Raman amplification, respectively. In contrast, digital back propagation (DBP) or OPC-only systems provide only 0.8 ~ 1.5 dB Q-factor gains.

Orbit-product representation and correction of Gaussian belief propagation

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

Johnson, Jason K; Chertkov, Michael; Chernyak, Vladimir

We present a new interpretation of Gaussian belief propagation (GaBP) based on the 'zeta function' representation of the determinant as a product over orbits of a graph. We show that GaBP captures back-tracking orbits of the graph and consider how to correct this estimate by accounting for non-backtracking orbits. We show that the product over non-backtracking orbits may be interpreted as the determinant of the non-backtracking adjacency matrix of the graph with edge weights based on the solution of GaBP. An efficient method is proposed to compute a truncated correction factor including all non-backtracking orbits up to a specified length.

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

NASA Astrophysics Data System (ADS)

Gui, Chun; Zhang, Ruisheng; Zhao, Zhili; Wei, Jiaxuan; Hu, Rongjing

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Sythesis of MCMC and Belief Propagation

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

Ahn, Sungsoo; Chertkov, Michael; Shin, Jinwoo

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach whichmore » allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.« less

A novel decoding algorithm based on the hierarchical reliable strategy for SCG-LDPC codes in optical communications

NASA Astrophysics Data System (ADS)

Yuan, Jian-guo; Tong, Qing-zhen; Huang, Sheng; Wang, Yong

2013-11-01

An effective hierarchical reliable belief propagation (HRBP) decoding algorithm is proposed according to the structural characteristics of systematically constructed Gallager low-density parity-check (SCG-LDPC) codes. The novel decoding algorithm combines the layered iteration with the reliability judgment, and can greatly reduce the number of the variable nodes involved in the subsequent iteration process and accelerate the convergence rate. The result of simulation for SCG-LDPC(3969,3720) code shows that the novel HRBP decoding algorithm can greatly reduce the computing amount at the condition of ensuring the performance compared with the traditional belief propagation (BP) algorithm. The bit error rate (BER) of the HRBP algorithm is considerable at the threshold value of 15, but in the subsequent iteration process, the number of the variable nodes for the HRBP algorithm can be reduced by about 70% at the high signal-to-noise ratio (SNR) compared with the BP algorithm. When the threshold value is further increased, the HRBP algorithm will gradually degenerate into the layered-BP algorithm, but at the BER of 10-7 and the maximal iteration number of 30, the net coding gain (NCG) of the HRBP algorithm is 0.2 dB more than that of the BP algorithm, and the average iteration times can be reduced by about 40% at the high SNR. Therefore, the novel HRBP decoding algorithm is more suitable for optical communication systems.

Modified multiblock partial least squares path modeling algorithm with backpropagation neural networks approach

NASA Astrophysics Data System (ADS)

Yuniarto, Budi; Kurniawan, Robert

2017-03-01

PLS Path Modeling (PLS-PM) is different from covariance based SEM, where PLS-PM use an approach based on variance or component, therefore, PLS-PM is also known as a component based SEM. Multiblock Partial Least Squares (MBPLS) is a method in PLS regression which can be used in PLS Path Modeling which known as Multiblock PLS Path Modeling (MBPLS-PM). This method uses an iterative procedure in its algorithm. This research aims to modify MBPLS-PM with Back Propagation Neural Network approach. The result is MBPLS-PM algorithm can be modified using the Back Propagation Neural Network approach to replace the iterative process in backward and forward step to get the matrix t and the matrix u in the algorithm. By modifying the MBPLS-PM algorithm using Back Propagation Neural Network approach, the model parameters obtained are relatively not significantly different compared to model parameters obtained by original MBPLS-PM algorithm.

Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II).

PubMed

Zhao, Guo; Wang, Hui; Liu, Gang; Wang, Zhiqiang

2016-09-21

An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results.

An algorithm for propagating the square-root covariance matrix in triangular form

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Choe, C. Y.

1976-01-01

A method for propagating the square root of the state error covariance matrix in lower triangular form is described. The algorithm can be combined with any triangular square-root measurement update algorithm to obtain a triangular square-root sequential estimation algorithm. The triangular square-root algorithm compares favorably with the conventional sequential estimation algorithm with regard to computation time.

[Managment of acute low back pain without trauma - an algorithm].

PubMed

Melcher, Carolin; Wegener, Bernd; Jansson, Volkmar; Mutschler, Wolf; Kanz, Karl-Georg; Birkenmaier, Christof

2018-05-14

Low back pain is a common problem for primary care providers, outpatient clinics and A&E departments. The predominant symptoms are those of so-called "unspecific back pain", but serious pathologies can be concealed by the clinical signs. Especially less experienced colleagues have problems in treating these patients, as - despite the multitude of recommendations and guidelines - there is no generally accepted algorithm. After a literature search (Medline/Cochrane), 158 articles were selected from 15,000 papers and classified according to their level of evidence. These were attuned to the clinical guidelines of the orthopaedic and pain-physician associations in Europe, North America and overseas and the experience of specialists at LMU Munich, in order to achieve consistency with literature recommendations, as well as feasibility in everyday clinical work and optimised with practical relevance. An algorithm was formed to provide the crucial differential diagnosis of lumbar back pain according to its clinical relevance and to provide a plan of action offering reasonable diagnostic and therapeutic steps. As a consequence of distinct binary decisions, low back patients should be treated at any given time according to the guidelines, with emergencies detected, unnecessary diagnostic testing and interventions averted and reasonable treatment initiated pursuant to the underlying pathology. In the context of the available evidence, a clinical algorithm has been developed that translates the complex diagnostic testing of acute low back pain into a transparent, structured and systematic guideline. Georg Thieme Verlag KG Stuttgart · New York.

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media

NASA Astrophysics Data System (ADS)

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A.

2016-07-01

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N2), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task.

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media.

PubMed

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A

2016-07-26

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N(2)), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task.

Collective Influence Algorithm to find influencers via optimal percolation in massively large social media

PubMed Central

Morone, Flaviano; Min, Byungjoon; Bo, Lin; Mari, Romain; Makse, Hernán A.

2016-01-01

We elaborate on a linear-time implementation of Collective-Influence (CI) algorithm introduced by Morone, Makse, Nature 524, 65 (2015) to find the minimal set of influencers in networks via optimal percolation. The computational complexity of CI is O(N log N) when removing nodes one-by-one, made possible through an appropriate data structure to process CI. We introduce two Belief-Propagation (BP) variants of CI that consider global optimization via message-passing: CI propagation (CIP) and Collective-Immunization-Belief-Propagation algorithm (CIBP) based on optimal immunization. Both identify a slightly smaller fraction of influencers than CI and, remarkably, reproduce the exact analytical optimal percolation threshold obtained in Random Struct. Alg. 21, 397 (2002) for cubic random regular graphs, leaving little room for improvement for random graphs. However, the small augmented performance comes at the expense of increasing running time to O(N2), rendering BP prohibitive for modern-day big-data. For instance, for big-data social networks of 200 million users (e.g., Twitter users sending 500 million tweets/day), CI finds influencers in 2.5 hours on a single CPU, while all BP algorithms (CIP, CIBP and BDP) would take more than 3,000 years to accomplish the same task. PMID:27455878

Localized water reverberation phases and its impact on back-projection images

NASA Astrophysics Data System (ADS)

Yue, H.; Castillo, J.; Yu, C.; Meng, L.; Zhan, Z.

2017-12-01

Coherent radiators imaged by back-projections (BP) are commonly interpreted as part of the rupture process. Nevertheless, artifacts introduced by structure related phases are rarely discriminated from the rupture process. In this study, we adopt the logic of empirical Greens' function analysis (EGF) to discriminate between rupture and structure effect. We re-examine the waveforms and BP images of the 2012 Mw 7.2 Indian Ocean earthquake and an EGF event (Mw 6.2). The P wave codas of both events present similar shape with characteristic period of approximately 10 s, which are back-projected as coherent radiators near the trench. S wave BP doesn't image energy radiation near the trench. We interpret those coda waves as localized water reverberation phases excited near the trench. We perform a 2D waveform modeling using realistic bathymetry model, and find that the sharp near-trench bathymetry traps the acoustic water waves forming localized reverberation phases. These waves can be imaged as coherent near-trench radiators with similar features as that in the observations. We present a set of methodology to discriminate between the rupture and propagation effects in BP images, which can serve as a criterion of subevent identification.

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

A Novel Latin Hypercube Algorithm via Translational Propagation

PubMed Central

Pan, Guang; Ye, Pengcheng

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the experimental designs used. Optimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties. However, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs via translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization. TPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block with a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the efficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much more efficient in terms of the computation time and has acceptable space-filling and projective properties. PMID:25276844

Label propagation algorithm for community detection based on node importance and label influence

NASA Astrophysics Data System (ADS)

Zhang, Xian-Kun; Ren, Jing; Song, Chen; Jia, Jia; Zhang, Qian

2017-09-01

Recently, the detection of high-quality community has become a hot spot in the research of social network. Label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity and is unnecessary to define objective function and the number of community in advance. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the community. For large-scale social network, this paper proposes a novel label propagation algorithm for community detection based on node importance and label influence (LPA_NI). The experiments with comparative algorithms on real-world networks and synthetic networks have shown that LPA_NI can significantly improve the quality of community detection and shorten the iteration period. Also, it has better accuracy and stability in the case of similar complexity.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

NASA Astrophysics Data System (ADS)

Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

2018-06-01

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Effective Multifocus Image Fusion Based on HVS and BP Neural Network

PubMed Central

Yang, Yong

2014-01-01

The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations. PMID:24683327

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

PubMed

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-04-21

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

PubMed Central

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-01-01

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

A total variation diminishing finite difference algorithm for sonic boom propagation models

NASA Technical Reports Server (NTRS)

Sparrow, Victor W.

1993-01-01

It is difficult to accurately model the rise phases of sonic boom waveforms with traditional finite difference algorithms because of finite difference phase dispersion. This paper introduces the concept of a total variation diminishing (TVD) finite difference method as a tool for accurately modeling the rise phases of sonic booms. A standard second order finite difference algorithm and its TVD modified counterpart are both applied to the one-way propagation of a square pulse. The TVD method clearly outperforms the non-TVD method, showing great potential as a new computational tool in the analysis of sonic boom propagation.

Belief propagation decoding of quantum channels by passing quantum messages

NASA Astrophysics Data System (ADS)

Renes, Joseph M.

2017-07-01

The belief propagation (BP) algorithm is a powerful tool in a wide range of disciplines from statistical physics to machine learning to computational biology, and is ubiquitous in decoding classical error-correcting codes. The algorithm works by passing messages between nodes of the factor graph associated with the code and enables efficient decoding of the channel, in some cases even up to the Shannon capacity. Here we construct the first BP algorithm which passes quantum messages on the factor graph and is capable of decoding the classical-quantum channel with pure state outputs. This gives explicit decoding circuits whose number of gates is quadratic in the code length. We also show that this decoder can be modified to work with polar codes for the pure state channel and as part of a decoder for transmitting quantum information over the amplitude damping channel. These represent the first explicit capacity-achieving decoders for non-Pauli channels.

Multi-Array Back-Projections of The 2015 Gorkha Earthquake With Physics-Based Aftershock Calibrations

NASA Astrophysics Data System (ADS)

Meng, L.; Zhang, A.; Yagi, Y.

2015-12-01

The 2015 Mw 7.8 Nepal-Gorkha earthquake with casualties of over 9,000 people is the most devastating disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Its rupture process is well imaged by the teleseismic MUSIC back-projections (BP). Here, we perform independent back-projections of high-frequency recordings (0.5-2 Hz) from the Australian seismic network (AU), the North America network (NA) and the European seismic network (EU), located in complementary orientations. Our results of all three arrays show unilateral linear rupture path to the east of the hypocenter. But the propagating directions and the inferred rupture speeds differ significantly among different arrays. To understand the spatial uncertainties of the BP analysis, we image four moderate-size (M5~6) aftershocks based on the timing correction derived from the alignment of the initial P-wave of the mainshock. We find that the apparent source locations inferred from BP are systematically biased along the source-array orientation, which can be explained by the uncertainty of the 3D velocity structure deviated from the 1D reference model (e.g. IASP91). We introduced a slowness error term in travel time as a first-order calibration that successfully mitigates the source location discrepancies of different arrays. The calibrated BP results of three arrays are mutually consistent and reveal a unilateral rupture propagating eastward at a speed of 2.7 km/s along the down-dip edge of the locked Himalaya thrust zone over ~ 150 km, in agreement with a narrow slip distribution inferred from finite source inversions.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays

NASA Astrophysics Data System (ADS)

Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng

2011-12-01

An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.

A Comparison of JPDA and Belief Propagation for Data Association in SSA

NASA Astrophysics Data System (ADS)

Rutten, M.; Williams, J.; Gordon, N.; Jah, M.; Baldwin, J.; Stauch, J.

2014-09-01

The process of initial orbit determination, or catalogue maintenance, using a set of unlabeled observations requires a method of choosing which observation was due to which object. Realities of imperfect sensors mean that the association must be made in the presence of both missed detections and false alarms. Data association is not only essential to processing observations it can also be one of the most significant computational bottlenecks. The constrained admissible region multiple hypothesis filter (CAR-MHF) is an algorithm for initial orbit determination using short-arc observations of space objects. CAR-MHF has used joint probabilistic data association (JPDA), a well-established approach to multi-target data association. A recent development in the target tracking literature is the use of graphical models to formulate data association problems. Using an approximate inference algorithm, belief propagation (BP), on the graphical model results in an algorithm this is both computationally efficient and accurate. This paper compares CAR-MHF using JPDA and CAR-MHF using BP for the problem of initial orbit determination on a set of deep-space objects. The results of the analysis will show that by using the BP algorithm there are significant gains in computational load without any statistically significant loss in overall performance of the orbit determination.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

NASA Astrophysics Data System (ADS)

Jin, Huijin; Wu, Sujun; Peng, Yuncheng

2013-12-01

In this study, an artificial neural network (ANN) was employed to predict the contact fatigue life of alloy cast steel rolls (ACSRs) as a function of alloy composition, heat treatment parameters, and contact stress by utilizing the back-propagation algorithm. The ANN was trained and tested using experimental data and a very good performance of the neural network was achieved. The well-trained neural network was then adopted to predict the contact fatigue life of chromium alloyed cast steel rolls with different alloy compositions and heat treatment processes. The prediction results showed that the maximum value of contact fatigue life was obtained with quenching at 960 °C, tempering at 520 °C, and under the contact stress of 2355 MPa. The optimal alloy composition was C-0.54, Si-0.66, Mn-0.67, Cr-4.74, Mo-0.46, V-0.13, Ni-0.34, and Fe-balance (wt.%). Some explanations of the predicted results from the metallurgical viewpoints are given. A convenient and powerful method of optimizing alloy composition and heat treatment parameters of ACSRs has been developed.

Optimizing hidden layer node number of BP network to estimate fetal weight

NASA Astrophysics Data System (ADS)

Su, Juan; Zou, Yuanwen; Lin, Jiangli; Wang, Tianfu; Li, Deyu; Xie, Tao

2007-12-01

The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm.

PubMed

Tan, Xia; Ji, Zhong; Zhang, Yadan

2018-04-25

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

Accurate orbit propagation in the presence of planetary close encounters

NASA Astrophysics Data System (ADS)

Amato, Davide; Baù, Giulio; Bombardelli, Claudio

2017-09-01

We present an efficient strategy for the numerical propagation of small Solar system objects undergoing close encounters with massive bodies. The trajectory is split into several phases, each of them being the solution of a perturbed two-body problem. Formulations regularized with respect to different primaries are employed in two subsequent phases. In particular, we consider the Kustaanheimo-Stiefel regularization and a novel set of non-singular orbital elements pertaining to the Dromo family. In order to test the proposed strategy, we perform ensemble propagations in the Earth-Sun Circular Restricted 3-Body Problem (CR3BP) using a variable step size and order multistep integrator and an improved version of Everhart's radau solver of 15th order. By combining the trajectory splitting with regularized equations of motion in short-term propagations (1 year), we gain up to six orders of magnitude in accuracy with respect to the classical Cowell's method for the same computational cost. Moreover, in the propagation of asteroid (99942) Apophis through its 2029 Earth encounter, the position error stays within 100 metres after 100 years. In general, as to improve the performance of regularized formulations, the trajectory must be split between 1.2 and 3 Hill radii from the Earth. We also devise a robust iterative algorithm to stop the integration of regularized equations of motion at a prescribed physical time. The results rigorously hold in the CR3BP, and similar considerations may apply when considering more complex models. The methods and algorithms are implemented in the naples fortran 2003 code, which is available online as a GitHub repository.

A Seed-Based Plant Propagation Algorithm: The Feeding Station Model

PubMed Central

Salhi, Abdellah

2015-01-01

The seasonal production of fruit and seeds is akin to opening a feeding station, such as a restaurant. Agents coming to feed on the fruit are like customers attending the restaurant; they arrive at a certain rate and get served at a certain rate following some appropriate processes. The same applies to birds and animals visiting and feeding on ripe fruit produced by plants such as the strawberry plant. This phenomenon underpins the seed dispersion of the plants. Modelling it as a queuing process results in a seed-based search/optimisation algorithm. This variant of the Plant Propagation Algorithm is described, analysed, tested on nontrivial problems, and compared with well established algorithms. The results are included. PMID:25821858

Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a back-propagation neural network algorithm

NASA Astrophysics Data System (ADS)

Han, Hyung-Suk

2012-12-01

The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as "Loudness" and "Annoyance", psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to develop a single index of "Loudness" and "Annoyance" for noise inside a naval vessel according to a psycho-acoustic evaluation by using psychological responses such as Noise Rating (NR), Noise Criterion (NC), Room Criterion (RC), Preferred Speech Interference Level (PSIL) and loudness level. Additionally, in order to determine a single index of satisfaction for noise such as "Loudness" and "Annoyance", with respect to a human's sense of hearing, a back-propagation neural network is applied.

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization

PubMed Central

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194

Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

PubMed

Huang, Daizheng; Wu, Zhihui

2017-01-01

Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

Self-learning fuzzy controllers based on temporal back propagation

NASA Technical Reports Server (NTRS)

Jang, Jyh-Shing R.

1992-01-01

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

Spread prediction model of continuous steel tube based on BP neural network

NASA Astrophysics Data System (ADS)

Zhai, Jian-wei; Yu, Hui; Zou, Hai-bei; Wang, San-zhong; Liu, Li-gang

2017-07-01

According to the geometric pass of roll and technological parameters of three-roller continuous mandrel rolling mill in a factory, a finite element model is established to simulate the continuous rolling process of seamless steel tube, and the reliability of finite element model is verified by comparing with the simulation results and actual results of rolling force, wall thickness and outer diameter of the tube. The effect of roller reduction, roller rotation speed and blooming temperature on the spread rule is studied. Based on BP(Back Propagation) neural network technology, a spread prediction model of continuous rolling tube is established for training wall thickness coefficient and spread coefficient of the continuous rolling tube, and the rapid and accurate prediction of continuous rolling tube size is realized.

Weighted compactness function based label propagation algorithm for community detection

NASA Astrophysics Data System (ADS)

Zhang, Weitong; Zhang, Rui; Shang, Ronghua; Jiao, Licheng

2018-02-01

Community detection in complex networks, is to detect the community structure with the internal structure relatively compact and the external structure relatively sparse, according to the topological relationship among nodes in the network. In this paper, we propose a compactness function which combines the weight of nodes, and use it as the objective function to carry out the node label propagation. Firstly, according to the node degree, we find the sets of core nodes which have great influence on the network. The more the connections between the core nodes and the other nodes are, the larger the amount of the information these kernel nodes receive and transform. Then, according to the similarity of the nodes between the core nodes sets and the nodes degree, we assign weights to the nodes in the network. So the label of the nodes with great influence will be the priority in the label propagation process, which effectively improves the accuracy of the label propagation. The compactness function between nodes and communities in this paper is based on the nodes influence. It combines the connections between nodes and communities with the degree of the node belongs to its neighbor communities based on calculating the node weight. The function effectively uses the information of nodes and connections in the network. The experimental results show that the proposed algorithm can achieve good results in the artificial network and large-scale real networks compared with the 8 contrast algorithms.

Scene segmentation of natural images using texture measures and back-propagation

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Phatak, Anil; Chatterji, Gano

1993-01-01

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Matching algorithm of missile tail flame based on back-propagation neural network

NASA Astrophysics Data System (ADS)

Huang, Da; Huang, Shucai; Tang, Yidong; Zhao, Wei; Cao, Wenhuan

2018-02-01

This work presents a spectral matching algorithm of missile plume detection that based on neural network. The radiation value of the characteristic spectrum of the missile tail flame is taken as the input of the network. The network's structure including the number of nodes and layers is determined according to the number of characteristic spectral bands and missile types. We can get the network weight matrixes and threshold vectors through training the network using training samples, and we can determine the performance of the network through testing the network using the test samples. A small amount of data cause the network has the advantages of simple structure and practicality. Network structure composed of weight matrix and threshold vector can complete task of spectrum matching without large database support. Network can achieve real-time requirements with a small quantity of data. Experiment results show that the algorithm has the ability to match the precise spectrum and strong robustness.

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

PubMed

Martínez-Blanco, Ma Del Rosario; Ornelas-Vargas, Gerardo; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, José M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

2016-11-01

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN. Copyright © 2016 Elsevier Ltd. All rights reserved.

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width.

PubMed

Learn, R; Feigenbaum, E

2016-06-01

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. The second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width

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

Learn, R.; Feigenbaum, E.

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. Furthermore, the second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

Adaptive step-size algorithm for Fourier beam-propagation method with absorbing boundary layer of auto-determined width

DOE PAGES

Learn, R.; Feigenbaum, E.

2016-05-27

Two algorithms that enhance the utility of the absorbing boundary layer are presented, mainly in the framework of the Fourier beam-propagation method. One is an automated boundary layer width selector that chooses a near-optimal boundary size based on the initial beam shape. Furthermore, the second algorithm adjusts the propagation step sizes based on the beam shape at the beginning of each step in order to reduce aliasing artifacts.

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

NASA Astrophysics Data System (ADS)

Berahmand, Kamal; Bouyer, Asgarali

2018-03-01

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

The effect of back and feet support on oscillometric blood pressure measurements.

PubMed

Ringrose, Jennifer S; Wong, Jonathan; Yousefi, Farahnaz; Padwal, Raj

2017-08-01

Recommendations to support the back and feet during blood pressure (BP) measurement are not always followed in clinical practice. Our objective was to determine to what extent back and feet support affects mean oscillometric BP measurements. Eighty-five consecutive, consenting participants 18 years or older with systolic BP readings 80-220 mmHg and diastolic BP readings 50-120 mmHg and arm circumferences of 25-43 cm were recruited. BP was measured using an Omron HEM 907 oscillometric device. Back and feet support were examined independently. First, while the feet were supported, two sets of three BP readings were taken in random order: one with the back supported and one with the back unsupported. Next, with the back supported, two sets of three BP readings were taken in random order: one with the feet dangling and one with feet supported. The mean age of the participants was 52.0±20.7 years and the mean arm circumference was 31.0±3.2 cm; 62% were women and 49% had hypertension. The mean BP levels with the back unsupported were slightly higher than those with the back supported (119.8±15.5/69.9±8.9 vs. 119.2±16.4/68.2±8.8 mmHg; difference of 0.7±4.9/1.8±3.0; P=0.21 for systolic and <0.0001 for diastolic comparisons). The mean BP levels with feet dangling were slightly lower than with feet supported (120.3±16.3/72.6±8.9 vs. 121.2±16.1/72.9±8.6 mmHg; difference of -0.9±4.1/-0.3±2.8; P=0.04 for systolic and <0.36 for diastolic comparisons). Systolic BP differences were greater than or equal to 5 mmHg in 34% (back phase) and 23% (feet phase) of the participants. Provision of back and feet support has a small effect on the mean oscillometric BP. The magnitude of effect is greatest on diastolic BP when the back is unsupported.

Algorithmic detectability threshold of the stochastic block model

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro

2018-03-01

The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectation-maximization (EM) algorithm with belief propagation (BP) and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Genetic algorithm for neural networks optimization

NASA Astrophysics Data System (ADS)

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

2004-11-01

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

Precise Aperture-Dependent Motion Compensation with Frequency Domain Fast Back-Projection Algorithm.

PubMed

Zhang, Man; Wang, Guanyong; Zhang, Lei

2017-10-26

Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped blocks concerning the azimuth-dependent residual errors. However, image domain post-filtering approaches, such as precise topography- and aperture-dependent motion compensation algorithm (PTA), have difficulty of robustness in declining, when strong motion errors are involved in the coarse-focused image. In this case, in order to capture the complete motion blurring function within each image block, both the block size and the overlapped part need necessary extension leading to degeneration of efficiency and robustness inevitably. Herein, a frequency domain fast back-projection algorithm (FDFBPA) is introduced to deal with strong azimuth-variant motion errors. FDFBPA disposes of the azimuth-variant motion errors based on a precise azimuth spectrum expression in the azimuth wavenumber domain. First, a wavenumber domain sub-aperture processing strategy is introduced to accelerate computation. After that, the azimuth wavenumber spectrum is partitioned into a set of wavenumber blocks, and each block is formed into a sub-aperture coarse resolution image via the back-projection integral. Then, the sub-aperture images are straightforwardly fused together in azimuth wavenumber domain to obtain a full resolution image. Moreover, chirp-Z transform (CZT) is also introduced to implement the sub-aperture back-projection integral, increasing the efficiency of the algorithm. By disusing the image domain post-filtering strategy, robustness of the proposed algorithm is improved. Both simulation and real-measured data experiments demonstrate the effectiveness and superiority of the proposal.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

PubMed Central

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

An intelligent switch with back-propagation neural network based hybrid power system

NASA Astrophysics Data System (ADS)

Perdana, R. H. Y.; Fibriana, F.

2018-03-01

The consumption of conventional energy such as fossil fuels plays the critical role in the global warming issues. The carbon dioxide, methane, nitrous oxide, etc. could lead the greenhouse effects and change the climate pattern. In fact, 77% of the electrical energy is generated from fossil fuels combustion. Therefore, it is necessary to use the renewable energy sources for reducing the conventional energy consumption regarding electricity generation. This paper presents an intelligent switch to combine both energy resources, i.e., the solar panels as the renewable energy with the conventional energy from the State Electricity Enterprise (PLN). The artificial intelligence technology with the back-propagation neural network was designed to control the flow of energy that is distributed dynamically based on renewable energy generation. By the continuous monitoring on each load and source, the dynamic pattern of the intelligent switch was better than the conventional switching method. The first experimental results for 60 W solar panels showed the standard deviation of the trial at 0.7 and standard deviation of the experiment at 0.28. The second operation for a 900 W of solar panel obtained the standard deviation of the trial at 0.05 and 0.18 for the standard deviation of the experiment. Moreover, the accuracy reached 83% using this method. By the combination of the back-propagation neural network with the observation of energy usage of the load using wireless sensor network, each load can be evenly distributed and will impact on the reduction of conventional energy usage.

Chromatic characterization of a three-channel colorimeter using back-propagation neural networks

NASA Astrophysics Data System (ADS)

Pardo, P. J.; Pérez, A. L.; Suero, M. I.

2004-09-01

This work describes a method for the chromatic characterization of a three-channel colorimeter of recent design and construction dedicated to color vision research. The colorimeter consists of two fixed monochromators and a third monochromator interchangeable with a cathode ray tube or any other external light source. Back-propagation neural networks were used for the chromatic characterization to establish the relationship between each monochromator's input parameters and the tristimulus values of each chromatic stimulus generated. The results showed the effectiveness of this type of neural-network-based system for the chromatic characterization of the stimuli produced by any monochromator.

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

PubMed Central

Chuan, He; Dishan, Qiu; Jin, Liu

2012-01-01

The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible. PMID:23365522

Feature selection and back-projection algorithms for nonline-of-sight laser-gated viewing

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Velten, Andreas

2014-11-01

We discuss new approaches to analyze laser-gated viewing data for nonline-of-sight vision with a frame-to-frame back-projection as well as feature selection algorithms. Although first back-projection approaches use time transients for each pixel, our method has the ability to calculate the projection of imaging data on the voxel space for each frame. Further, different data analysis algorithms and their sequential application were studied with the aim of identifying and selecting signals from different target positions. A slight modification of commonly used filters leads to a powerful selection of local maximum values. It is demonstrated that the choice of the filter has an impact on the selectivity i.e., multiple target detection as well as on the localization precision.

Blood pressure (BP) assessment-from BP level to BP variability.

PubMed

Feber, Janusz; Litwin, Mieczyslaw

2016-07-01

The assessment of blood pressure (BP) can be challenging in children, especially in very young individuals, due to their variable body size and lack of cooperation. In the absence of data relating BP with cardiovascular outcomes in children, there is a need to convert absolute BP values (in mmHg) into age-, gender- and height appropriate BP percentiles or Z-scores in order to compare a patient's BP with the BP of healthy children of the same age, but also of children of different ages. Traditionally, the interpretation of BP has been based mainly on the assessment of the BP level obtained by office, home or 24-h BP monitoring. Recent studies suggest that it is not only BP level (i.e. average BP) but also BP variability that is clinically important for the development of target organ damage, including the progression of chronic kidney disease. In this review we describe current methods to evaluate of BP level, outline available methods for BP variability assessment and discuss the clinical consequences of BP variability, including its potential role in the management of hypertension.

Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-Jacobi formulations

NASA Technical Reports Server (NTRS)

Osher, Stanley; Sethian, James A.

1987-01-01

New numerical algorithms are devised (PSC algorithms) for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front can also be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand-sides, by using techniques from the hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be used also for more general Hamilton-Jacobi-type problems. The algorithms are demonstrated by computing the solution to a variety of surface motion problems.

Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins.

PubMed

Donovan-Maiye, Rory M; Langmead, Christopher J; Zuckerman, Daniel M

2018-01-09

Motivated by the extremely high computing costs associated with estimates of free energies for biological systems using molecular simulations, we further the exploration of existing "belief propagation" (BP) algorithms for fixed-backbone peptide and protein systems. The precalculation of pairwise interactions among discretized libraries of side-chain conformations, along with representation of protein side chains as nodes in a graphical model, enables direct application of the BP approach, which requires only ∼1 s of single-processor run time after the precalculation stage. We use a "loopy BP" algorithm, which can be seen as an approximate generalization of the transfer-matrix approach to highly connected (i.e., loopy) graphs, and it has previously been applied to protein calculations. We examine the application of loopy BP to several peptides as well as the binding site of the T4 lysozyme L99A mutant. The present study reports on (i) the comparison of the approximate BP results with estimates from unbiased estimators based on the Amber99SB force field; (ii) investigation of the effects of varying library size on BP predictions; and (iii) a theoretical discussion of the discretization effects that can arise in BP calculations. The data suggest that, despite their approximate nature, BP free-energy estimates are highly accurate-indeed, they never fall outside confidence intervals from unbiased estimators for the systems where independent results could be obtained. Furthermore, we find that libraries of sufficiently fine discretization (which diminish library-size sensitivity) can be obtained with standard computing resources in most cases. Altogether, the extremely low computing times and accurate results suggest the BP approach warrants further study.

One-dimensional inversion of geo-electrical resistivity sounding data using artificial neural networks—a case study

NASA Astrophysics Data System (ADS)

Singh, U. K.; Tiwari, R. K.; Singh, S. B.

2005-02-01

This paper deals with the application of artificial neural networks (ANN) technique for the study of a case history using 1-D inversion of vertical electrical resistivity sounding (VES) data from the Puga valley, Kashmir, India. The study area is important for its rich geothermal resources as well as from the tectonic point of view as it is located near the collision boundary of the Indo-Asian crustal plates. In order to understand the resistivity structure and layer thicknesses, we used here three-layer feedforward neural networks to model and predict measured VES data. Three algorithms, e.g. back-propagation (BP), adaptive back-propagation (ABP) and Levenberg-Marquardt algorithm (LMA) were applied to the synthetic as well as real VES field data and efficiency of supervised training network are compared. Analyses suggest that LMA is computationally faster and give results, which are comparatively more accurate and consistent than BP and ABP. The results obtained using the ANN inversions are remarkably correlated with the available borehole litho-logs. The feasibility study suggests that ANN methods offer an excellent complementary tool for the direct detection of layered resistivity structure.

Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi™.

PubMed

Gomes, Jeremias M; Teodoro, George; de Melo, Alba; Kong, Jun; Kurc, Tahsin; Saltz, Joel H

2015-10-01

We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel ® Xeon Phi ™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP's irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63 × on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7 × and 1.62 × , respectively, as compared to efficient CPU and GPU implementations.

Efficient irregular wavefront propagation algorithms on Intel® Xeon Phi™

PubMed Central

Gomes, Jeremias M.; Teodoro, George; de Melo, Alba; Kong, Jun; Kurc, Tahsin; Saltz, Joel H.

2016-01-01

We investigate the execution of the Irregular Wavefront Propagation Pattern (IWPP), a fundamental computing structure used in several image analysis operations, on the Intel® Xeon Phi™ co-processor. An efficient implementation of IWPP on the Xeon Phi is a challenging problem because of IWPP’s irregularity and the use of atomic instructions in the original IWPP algorithm to resolve race conditions. On the Xeon Phi, the use of SIMD and vectorization instructions is critical to attain high performance. However, SIMD atomic instructions are not supported. Therefore, we propose a new IWPP algorithm that can take advantage of the supported SIMD instruction set. We also evaluate an alternate storage container (priority queue) to track active elements in the wavefront in an effort to improve the parallel algorithm efficiency. The new IWPP algorithm is evaluated with Morphological Reconstruction and Imfill operations as use cases. Our results show performance improvements of up to 5.63× on top of the original IWPP due to vectorization. Moreover, the new IWPP achieves speedups of 45.7× and 1.62×, respectively, as compared to efficient CPU and GPU implementations. PMID:27298591

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

NASA Astrophysics Data System (ADS)

Li, Zhaokun; Zhao, Xiaohui

2017-02-01

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

Fast algorithms for transforming back and forth between a signed permutation and its equivalent simple permutation.

PubMed

Gog, Simon; Bader, Martin

2008-10-01

The problem of sorting signed permutations by reversals is a well-studied problem in computational biology. The first polynomial time algorithm was presented by Hannenhalli and Pevzner in 1995. The algorithm was improved several times, and nowadays the most efficient algorithm has a subquadratic running time. Simple permutations played an important role in the development of these algorithms. Although the latest result of Tannier et al. does not require simple permutations, the preliminary version of their algorithm as well as the first polynomial time algorithm of Hannenhalli and Pevzner use the structure of simple permutations. More precisely, the latter algorithms require a precomputation that transforms a permutation into an equivalent simple permutation. To the best of our knowledge, all published algorithms for this transformation have at least a quadratic running time. For further investigations on genome rearrangement problems, the existence of a fast algorithm for the transformation could be crucial. Another important task is the back transformation, i.e. if we have a sorting on the simple permutation, transform it into a sorting on the original permutation. Again, the naive approach results in an algorithm with quadratic running time. In this paper, we present a linear time algorithm for transforming a permutation into an equivalent simple permutation, and an O(n log n) algorithm for the back transformation of the sorting sequence.

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

PubMed Central

Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

2016-01-01

In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. PMID:27123002

Optimizing Thermal-Elastic Properties of C/C–SiC Composites Using a Hybrid Approach and PSO Algorithm

PubMed Central

Xu, Yingjie; Gao, Tian

2016-01-01

Carbon fiber-reinforced multi-layered pyrocarbon–silicon carbide matrix (C/C–SiC) composites are widely used in aerospace structures. The complicated spatial architecture and material heterogeneity of C/C–SiC composites constitute the challenge for tailoring their properties. Thus, discovering the intrinsic relations between the properties and the microstructures and sequentially optimizing the microstructures to obtain composites with the best performances becomes the key for practical applications. The objective of this work is to optimize the thermal-elastic properties of unidirectional C/C–SiC composites by controlling the multi-layered matrix thicknesses. A hybrid approach based on micromechanical modeling and back propagation (BP) neural network is proposed to predict the thermal-elastic properties of composites. Then, a particle swarm optimization (PSO) algorithm is interfaced with this hybrid model to achieve the optimal design for minimizing the coefficient of thermal expansion (CTE) of composites with the constraint of elastic modulus. Numerical examples demonstrate the effectiveness of the proposed hybrid model and optimization method. PMID:28773343

Propagation of back-arc extension into the arc lithosphere in the southern New Hebrides volcanic arc

NASA Astrophysics Data System (ADS)

Patriat, M.; Collot, J.; Danyushevsky, L.; Fabre, M.; Meffre, S.; Falloon, T.; Rouillard, P.; Pelletier, B.; Roach, M.; Fournier, M.

2015-09-01

New geophysical data acquired during three expeditions of the R/V Southern Surveyor in the southern part of the North Fiji Basin allow us to characterize the deformation of the upper plate at the southern termination of the New Hebrides subduction zone, where it bends eastward along the Hunter Ridge. Unlike the northern end of the Tonga subduction zone, on the other side of the North Fiji Basin, the 90° bend does not correspond to the transition from a subduction zone to a transform fault, but it is due to the progressive retreat of the New Hebrides trench. The subduction trench retreat is accommodated in the upper plate by the migration toward the southwest of the New Hebrides arc and toward the south of the Hunter Ridge, so that the direction of convergence remains everywhere orthogonal to the trench. In the back-arc domain, the active deformation is characterized by propagation of the back-arc spreading ridge into the Hunter volcanic arc. The N-S spreading axis propagates southward and penetrates in the arc, where it connects to a sinistral strike-slip zone via an oblique rift. The collision of the Loyalty Ridge with the New Hebrides arc, less than two million years ago, likely initiated this deformation pattern and the fragmentation of the upper plate. In this particular geodynamic setting, with an oceanic lithosphere subducting beneath a highly sheared volcanic arc, a wide range of primitive subduction-related magmas has been produced including adakites, island arc tholeiites, back-arc basin basalts, and medium-K subduction-related lavas.

Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

PubMed

Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

2013-08-26

We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Oppositely directed pairs of propagating rifts in back-arc basins: Double saloon door seafloor spreading during subduction rollback

NASA Astrophysics Data System (ADS)

Martin, A. K.

2006-06-01

When a continent breaks up into two plates, which then separate from each other about a rotation pole, it can be shown that if initial movement is taken up by lithospheric extension, asthenospheric breakthrough and oceanic accretion propagate toward the pole of rotation. Such a propagating rift model is then applied to an embryonic centrally located rift which evolves into two rifts propagating in opposite directions. The resultant rhombic shape of the modeled basin, initially underlain entirely by thinned continental crust, is very similar to the Oligocene to Burdigalian back-arc evolution of the Valencia Trough and the Liguro-Provencal Basin in the western Mediterranean. Existing well and seismic stratigraphic data confirm that a rift did initiate in the Gulf of Lion and propagated southwest into the Valencia Trough. Similarly, seismic refraction, gravity, and heat flow data demonstrate that maximum extension within the Valencia Trough/Liguro-Provencal Basin occurred in an axial position close to the North Balearic Fracture Zone. The same model of oppositely propagating rifts, when applied to the Burdigalian/Langhian episode of back-arc oceanic accretion within the Liguro-Provencal and Algerian basins, predicts a number of features which are borne out by existing geological and geophysical, particularly magnetic data. These include the orientation of subparallel magnetic anomalies, presumed to be seafloor spreading isochrons, in both basins; concave-to-the-west fracture zones southwest of the North Balearic Fracture Zone, and concave-to-the-east fracture zones to its northeast; a spherical triangular area of NW oriented seafloor spreading isochrons southwest of Sardinia; the greater NW extension of the central (youngest?) magnetic anomaly within this triangular area, in agreement with the model-predicted northwestward propagation of a rift in this zone; successively more central (younger) magnetic anomalies abutting thinned continental crust nearer to the pole of

Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

NASA Astrophysics Data System (ADS)

Mu, Tao-tao; Chen, Si-ying; Zhang, Yin-chao; Guo, Pan; Chen, He; Zhang, Hong-yan; Liu, Xiao-hua; Wang, Yuan; Bu, Zhi-chao

2013-09-01

In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.

A PML-FDTD ALGORITHM FOR SIMULATING PLASMA-COVERED CAVITY-BACKED SLOT ANTENNAS. (R825225)

EPA Science Inventory

A three-dimensional frequency-dependent finite-difference time-domain (FDTD) algorithm with perfectly matched layer (PML) absorbing boundary condition (ABC) and recursive convolution approaches is developed to model plasma-covered open-ended waveguide or cavity-backed slot antenn...

Efficacy of an amlodipine/olmesartan treatment algorithm in patients with or without type 2 diabetes and hypertension (a secondary analysis of the BP-CRUSH study).

PubMed

Nesbitt, S D; Shojaee, A; Maa, J-F; Weir, M R

2013-07-01

A prespecified subgroup analysis of an open-label, multicenter, single-arm, dose-titration study is presented. The efficacy and safety of 20-week treatment with an amlodipine (AML)/olmesartan medoxomil (OM)±hydrochlorothiazide (HCTZ) algorithm were assessed in patients with hypertension and type 2 diabetes mellitus (T2DM) who were uncontrolled by antihypertensive monotherapy. Eligible patients received AML/OM 5/20 mg for 4 weeks, followed by stepwise uptitration to AML/OM 5/40 mg, AML/OM 10/40 mg, AML/OM 10/40 mg+HCTZ 12.5 mg and AML/OM 10/40 mg+HCTZ 25 mg at 4-week intervals if blood pressure (BP) remained uncontrolled. The primary end point was the achievement of the seated cuff systolic BP (SeSBP) goal (<140 mm Hg, or <130 mm Hg for patients with T2DM) at week 12. Seated cuff BP was significantly reduced from baseline at all titration dose periods. At week 12, the cumulative SeSBP goal was achieved by 57.9% and 80.1% of patients in the T2DM and non-T2DM subgroups, respectively. Treatment was well tolerated, with low rates of peripheral edema. In summary, switching to a treatment algorithm based on AML/OM±HCTZ after failed monotherapy was safe and improved BP control in patients with hypertension and T2DM.

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

A new linear back projection algorithm to electrical tomography based on measuring data decomposition

NASA Astrophysics Data System (ADS)

Sun, Benyuan; Yue, Shihong; Cui, Ziqiang; Wang, Huaxiang

2015-12-01

As an advanced measurement technique of non-radiant, non-intrusive, rapid response, and low cost, the electrical tomography (ET) technique has developed rapidly in recent decades. The ET imaging algorithm plays an important role in the ET imaging process. Linear back projection (LBP) is the most used ET algorithm due to its advantages of dynamic imaging process, real-time response, and easy realization. But the LBP algorithm is of low spatial resolution due to the natural ‘soft field’ effect and ‘ill-posed solution’ problems; thus its applicable ranges are greatly limited. In this paper, an original data decomposition method is proposed, and every ET measuring data are decomposed into two independent new data based on the positive and negative sensing areas of the measuring data. Consequently, the number of total measuring data is extended to twice as many as the number of the original data, thus effectively reducing the ‘ill-posed solution’. On the other hand, an index to measure the ‘soft field’ effect is proposed. The index shows that the decomposed data can distinguish between different contributions of various units (pixels) for any ET measuring data, and can efficiently reduce the ‘soft field’ effect of the ET imaging process. In light of the data decomposition method, a new linear back projection algorithm is proposed to improve the spatial resolution of the ET image. A series of simulations and experiments are applied to validate the proposed algorithm by the real-time performances and the progress of spatial resolutions.

Application of affinity propagation algorithm based on manifold distance for transformer PD pattern recognition

NASA Astrophysics Data System (ADS)

Wei, B. G.; Huo, K. X.; Yao, Z. F.; Lou, J.; Li, X. Y.

2018-03-01

It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network.

PubMed

Huang, Li; Huang, Jian; Wang, Wei

2018-01-18

Resettlement affects not only the resettlers' production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers' sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries.

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network

PubMed Central

Huang, Li; Huang, Jian

2018-01-01

Resettlement affects not only the resettlers’ production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers’ sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries. PMID:29346305

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-01

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp = 0.9180 and RMSEP = 2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

NASA Astrophysics Data System (ADS)

Zhu, Xiaoliang; Du, Li; Liu, Bendong; Zhe, Jiang

2016-06-01

We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g-1), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

PubMed

Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network

PubMed Central

Song, Xianzhi; Peng, Chi; Li, Gensheng

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID

78 FR 60270 - BP America Inc., BP Corporation North America Inc., BP America Production Company, and BP Energy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-01

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. IN13-15-000] BP America Inc., BP Corporation North America Inc., BP America Production Company, and BP Energy Company; Notice of Designation of Commission Staff as Non-Decisional With respect to an order issued by the Commission on August...

Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

NASA Astrophysics Data System (ADS)

Yu, Yali; Wang, Mengxia; Lima, Dimas

2018-04-01

In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

Multiple Time-Step Dual-Hamiltonian Hybrid Molecular Dynamics — Monte Carlo Canonical Propagation Algorithm

PubMed Central

Weare, Jonathan; Dinner, Aaron R.; Roux, Benoît

2016-01-01

A multiple time-step integrator based on a dual Hamiltonian and a hybrid method combining molecular dynamics (MD) and Monte Carlo (MC) is proposed to sample systems in the canonical ensemble. The Dual Hamiltonian Multiple Time-Step (DHMTS) algorithm is based on two similar Hamiltonians: a computationally expensive one that serves as a reference and a computationally inexpensive one to which the workload is shifted. The central assumption is that the difference between the two Hamiltonians is slowly varying. Earlier work has shown that such dual Hamiltonian multiple time-step schemes effectively precondition nonlinear differential equations for dynamics by reformulating them into a recursive root finding problem that can be solved by propagating a correction term through an internal loop, analogous to RESPA. Of special interest in the present context, a hybrid MD-MC version of the DHMTS algorithm is introduced to enforce detailed balance via a Metropolis acceptance criterion and ensure consistency with the Boltzmann distribution. The Metropolis criterion suppresses the discretization errors normally associated with the propagation according to the computationally inexpensive Hamiltonian, treating the discretization error as an external work. Illustrative tests are carried out to demonstrate the effectiveness of the method. PMID:26918826

Prevalence and tracking of back pain from childhood to adolescence

PubMed Central

2011-01-01

Background It is generally acknowledged that back pain (BP) is a common condition already in childhood. However, the development until early adulthood is not well understood and, in particular, not the individual tracking pattern. The objectives of this paper are to show the prevalence estimates of BP, low back pain (LBP), mid back pain (MBP), neck pain (NP), and care-seeking because of BP at three different ages (9, 13 and15 years) and how the BP reporting tracks over these age groups over three consecutive surveys. Methods A longitudinal cohort study was carried out from the years of 1997 till 2005, collecting interview data from children who were sampled to be representative of Danish schoolchildren. BP was defined overall and specifically in the three spinal regions as having reported pain within the past month. The prevalence estimates and the various patterns of BP reporting over time are presented as percentages. Results Of the 771 children sampled, 62%, 57%, and 58% participated in the three back surveys and 34% participated in all three. The prevalence estimates for children at the ages of 9, 13, and 15, respectively, were for BP 33%, 28%, and 48%; for LBP 4%, 22%, and 36%; for MBP 20%, 13%, and 35%; and for NP 10%, 7%, and 15%. Seeking care for BP increased from 6% and 8% at the two youngest ages to 34% at the oldest. Only 7% of the children who participated in all three surveys reported BP each time and 30% of these always reported no pain. The patterns of development differed for the three spinal regions and between genders. Status at the previous survey predicted status at the next survey, so that those who had pain before were more likely to report pain again and vice versa. This was most pronounced for care-seeking. Conclusion It was confirmed that BP starts early in life, but the patterns of onset and development over time vary for different parts of the spine and between genders. Because of these differences, it is recommended to report on BP in

A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm

NASA Astrophysics Data System (ADS)

Auroux, D.; Blum, J.

2008-03-01

This paper deals with a new data assimilation algorithm, called Back and Forth Nudging. The standard nudging technique consists in adding to the equations of the model a relaxation term that is supposed to force the observations to the model. The BFN algorithm consists in repeatedly performing forward and backward integrations of the model with relaxation (or nudging) terms, using opposite signs in the direct and inverse integrations, so as to make the backward evolution numerically stable. This algorithm has first been tested on the standard Lorenz model with discrete observations (perfect or noisy) and compared with the variational assimilation method. The same type of study has then been performed on the viscous Burgers equation, comparing again with the variational method and focusing on the time evolution of the reconstruction error, i.e. the difference between the reference trajectory and the identified one over a time period composed of an assimilation period followed by a prediction period. The possible use of the BFN algorithm as an initialization for the variational method has also been investigated. Finally the algorithm has been tested on a layered quasi-geostrophic model with sea-surface height observations. The behaviours of the two algorithms have been compared in the presence of perfect or noisy observations, and also for imperfect models. This has allowed us to reach a conclusion concerning the relative performances of the two algorithms.

Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

NASA Astrophysics Data System (ADS)

Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

2018-01-01

In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics

NASA Astrophysics Data System (ADS)

Zheng, Huaqing; Jiang, Minlan

2016-01-01

PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.

Optimization of training backpropagation algorithm using nguyen widrow for angina ludwig diagnosis

NASA Astrophysics Data System (ADS)

Aisyah, Siti; Harahap, Mawaddah; Mahmud Husein Siregar, Amir; Turnip, Mardi

2018-04-01

Tooth and mouth disease is a common disease, with a prevalence of more than 40% (children aged less than 7 years) in milk teeth and about 85% (adults aged 17 years and over) on permanent teeth. Angina Ludwig is one of mouth disease type that occurs due to infection of the tooth root and trauma of the mouth. ‘In this study back propagation algorithm applied to diagnose AnginaLudwig disease (using Nguyen Widrow method in optimization of training time). From the experimental results, it is known that the average BPNN by using Nguyen Widrow is much faster which is about 0.0624 seconds and 0.1019 seconds (without NguyenWidrow). In contrast, for pattern recognition needs, found that back propagation without Nguyen Widrow is much better that is with 90% accuracy (only 70% with NguyenWidrow).

A modified adaptive algorithm of dealing with the high chirp when chirped pulses propagating in optical fiber

NASA Astrophysics Data System (ADS)

Wu, Lianglong; Fu, Xiquan; Guo, Xing

2013-03-01

In this paper, we propose a modified adaptive algorithm (MAA) of dealing with the high chirp to efficiently simulate the propagation of chirped pulses along an optical fiber for the propagation distance shorter than the "temporal focal length". The basis of the MAA is that the chirp term of initial pulse is treated as the rapidly varying part by means of the idea of the slowly varying envelope approximation (SVEA). Numerical simulations show that the performance of the MAA is validated, and that the proposed method can decrease the number of sampling points by orders of magnitude. In addition, the computational efficiency of the MAA compared with the time-domain beam propagation method (BPM) can be enhanced with the increase of the chirp of initial pulse.

Metric Ranking of Invariant Networks with Belief Propagation

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

Tao, Changxia; Ge, Yong; Song, Qinbao

The management of large-scale distributed information systems relies on the effective use and modeling of monitoring data collected at various points in the distributed information systems. A promising approach is to discover invariant relationships among the monitoring data and generate invariant networks, where a node is a monitoring data source (metric) and a link indicates an invariant relationship between two monitoring data. Such an invariant network representation can help system experts to localize and diagnose the system faults by examining those broken invariant relationships and their related metrics, because system faults usually propagate among the monitoring data and eventually leadmore » to some broken invariant relationships. However, at one time, there are usually a lot of broken links (invariant relationships) within an invariant network. Without proper guidance, it is difficult for system experts to manually inspect this large number of broken links. Thus, a critical challenge is how to effectively and efficiently rank metrics (nodes) of invariant networks according to the anomaly levels of metrics. The ranked list of metrics will provide system experts with useful guidance for them to localize and diagnose the system faults. To this end, we propose to model the nodes and the broken links as a Markov Random Field (MRF), and develop an iteration algorithm to infer the anomaly of each node based on belief propagation (BP). Finally, we validate the proposed algorithm on both realworld and synthetic data sets to illustrate its effectiveness.« less

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools.

PubMed

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-05

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp=0.9180 and RMSEP=2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

A label distance maximum-based classifier for multi-label learning.

PubMed

Liu, Xiaoli; Bao, Hang; Zhao, Dazhe; Cao, Peng

2015-01-01

Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.

Efficient Geometric Sound Propagation Using Visibility Culling

NASA Astrophysics Data System (ADS)

Chandak, Anish

2011-07-01

Simulating propagation of sound can improve the sense of realism in interactive applications such as video games and can lead to better designs in engineering applications such as architectural acoustics. In this thesis, we present geometric sound propagation techniques which are faster than prior methods and map well to upcoming parallel multi-core CPUs. We model specular reflections by using the image-source method and model finite-edge diffraction by using the well-known Biot-Tolstoy-Medwin (BTM) model. We accelerate the computation of specular reflections by applying novel visibility algorithms, FastV and AD-Frustum, which compute visibility from a point. We accelerate finite-edge diffraction modeling by applying a novel visibility algorithm which computes visibility from a region. Our visibility algorithms are based on frustum tracing and exploit recent advances in fast ray-hierarchy intersections, data-parallel computations, and scalable, multi-core algorithms. The AD-Frustum algorithm adapts its computation to the scene complexity and allows small errors in computing specular reflection paths for higher computational efficiency. FastV and our visibility algorithm from a region are general, object-space, conservative visibility algorithms that together significantly reduce the number of image sources compared to other techniques while preserving the same accuracy. Our geometric propagation algorithms are an order of magnitude faster than prior approaches for modeling specular reflections and two to ten times faster for modeling finite-edge diffraction. Our algorithms are interactive, scale almost linearly on multi-core CPUs, and can handle large, complex, and dynamic scenes. We also compare the accuracy of our sound propagation algorithms with other methods. Once sound propagation is performed, it is desirable to listen to the propagated sound in interactive and engineering applications. We can generate smooth, artifact-free output audio signals by applying

A lithology identification method for continental shale oil reservoir based on BP neural network

NASA Astrophysics Data System (ADS)

Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

2018-06-01

The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

The prediction in computer color matching of dentistry based on GA+BP neural network.

PubMed

Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

2015-01-01

Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

Incidence of back pain in adolescent athletes: a prospective study.

PubMed

Mueller, Steffen; Mueller, Juliane; Stoll, Josefine; Prieske, Olaf; Cassel, Michael; Mayer, Frank

2016-01-01

Recently, the incidence rate of back pain (BP) in adolescents has been reported at 21%. However, the development of BP in adolescent athletes is unclear. Hence, the purpose of this study was to examine the incidence of BP in young elite athletes in relation to gender and type of sport practiced. Subjective BP was assessed in 321 elite adolescent athletes (m/f 57%/43%; 13.2 ± 1.4 years; 163.4 ± 11.4 cm; 52.6 ± 12.6 kg; 5.0 ± 2.6 training yrs; 7.6 ± 5.3 training h/week). Initially, all athletes were free of pain. The main outcome criterion was the incidence of back pain [%] analyzed in terms of pain development from the first measurement day (M1) to the second measurement day (M2) after 2.0 ± 1.0 year. Participants were classified into athletes who developed back pain (BPD) and athletes who did not develop back pain (nBPD). BP (acute or within the last 7 days) was assessed with a 5-step face scale (face 1-2 = no pain; face 3-5 = pain). BPD included all athletes who reported faces 1 and 2 at M1 and faces 3 to 5 at M2. nBPD were all athletes who reported face 1 or 2 at both M1 and M2. Data was analyzed descriptively. Additionally, a Chi 2 test was used to analyze gender- and sport-specific differences ( p  = 0.05). Thirty-two athletes were categorized as BPD (10%). The gender difference was 5% (m/f: 12%/7%) but did not show statistical significance ( p  = 0.15). The incidence of BP ranged between 6 and 15% for the different sport categories. Game sports (15%) showed the highest, and explosive strength sports (6%) the lowest incidence. Anthropometrics or training characteristics did not significantly influence BPD ( p  = 0.14 gender to p  = 0.90 sports; r 2  = 0.0825). BP incidence was lower in adolescent athletes compared to young non-athletes and even to the general adult population. Consequently, it can be concluded that high-performance sports do not lead to an additional increase in back pain incidence

Pruning Neural Networks with Distribution Estimation Algorithms

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

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Investigation of frame-to-frame back projection and feature selection algorithms for non-line-of-sight laser gated viewing

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Velten, Andreas

2014-10-01

In the present paper, we discuss new approaches to analyze laser gated viewing data for non-line-of-sight vision with a novel frame-to-frame back projection as well as feature selection algorithms. While first back projection approaches use time transients for each pixel, our new method has the ability to calculate the projection of imaging data on the obscured voxel space for each frame. Further, four different data analysis algorithms were studied with the aim to identify and select signals from different target positions. A slight modification of commonly used filters leads to powerful selection of local maximum values. It is demonstrated that the choice of the filter has impact on the selectivity i.e. multiple target detection as well as on the localization precision.

Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

NASA Astrophysics Data System (ADS)

Wang, Zheng; Mao, Zhihua; Xia, Junshi; Du, Peijun; Shi, Liangliang; Huang, Haiqing; Wang, Tianyu; Gong, Fang; Zhu, Qiankun

2018-06-01

The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of wide field, high temporal resolution, and short repeat cycle. However, this instrument suffers from its use of only four relatively low-quality bands which can't adequately resolve the features of long wavelengths. The Landsat Enhanced Thematic Mapper-plus (ETM+) provides high-quality data, however, the Scan Line Corrector (SLC) stopped working and caused striping of remote sensed images, which dramatically reduced the coverage of the ETM+ data. In order to combine the advantages of the HJ-CCD and Landsat ETM+ data, we adopted a back-propagation artificial neural network (BP-ANN) to fuse these two data types for this study. The results showed that the fused output data not only have the advantage of data intactness for the HJ-CCD, but also have the advantages of the multi-spectral and high radiometric resolution of the ETM+ data. Moreover, the fused data were analyzed qualitatively, quantitatively and from a practical application point of view. Experimental studies indicated that the fused data have a full spatial distribution, multi-spectral bands, high radiometric resolution, a small difference between the observed and fused output data, and a high correlation between the observed and fused data. The excellent performance in its practical application is a further demonstration that the fused data are of high quality.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

PubMed

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

SU-F-J-88: Comparison of Two Deformable Image Registration Algorithms for CT-To-CT Contour Propagation

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

Gopal, A; Xu, H; Chen, S

Purpose: To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling. Methods: Both DIR algorithms were used for CT-to-CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagationmore » was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR. Conclusion: The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.« less

A novel computational approach "BP-STOCH" to study ligand binding to finite lattice.

PubMed

Beshnova, Daria A; Bereznyak, Ekaterina G; Shestopalova, Anna V; Evstigneev, Maxim P

2011-03-01

We report a novel computational algorithm "BP-STOCH" to be used for studying single-type ligand binding with biopolymers of finite lengths, such as DNA oligonucleotides or oligopeptides. It is based on an idea to represent any type of ligand-biopolymer complex in a form of binary number, where "0" and "1" bits stand for vacant and engaged monomers of the biopolymer, respectively. Cycling over all binary numbers from the lowest 0 up to the highest 2(N) - 1 means a sequential generating of all possible configurations of vacant/engaged monomers, which, after proper filtering, results in a full set of possible types of complexes in solution between the ligand and the N-site lattice. The principal advantage of BP-STOCH algorithm is the possibility to incorporate into this cycle any conditions on computation of the concentrations and observed experimental parameters of the complexes in solution, and programmatic access to each monomer of the biopolymer within each binding site of every binding configuration. The latter is equivalent to unlimited extension of the basic reaction scheme and allows to use BP-STOCH algorithm as an alternative to conventional computational approaches.

Effect of back massage intervention on anxiety, comfort, and physiologic responses in patients with congestive heart failure.

PubMed

Chen, Wei-Ling; Liu, Gin-Jen; Yeh, Shu-Hui; Chiang, Ming-Chu; Fu, Mao-Young; Hsieh, Yuan-Kai

2013-05-01

Patients suffering from congestive heart failure (CHF) frequently feel physical suffering and anxiety. The researchers investigated whether back massage could reduce anxiety, discomfort, and physical suffering in patients with CHF. The effects of gender and severity-dependent response of back massage on anxiety and discomfort in patients were also analyzed. The study used a quasi-experimental design with one group pretest and posttest. Sixty-four participants were recruited in southern Taiwan. The modified State Anxiety Inventory, the discomfort Visual Analogue Scale, electronic blood pressure (BP) gauges, stethoscopes and the pulse oximetry were used in this study. The participants' systolic BP (F (3, 189)=18.91, p<0.01), diastolic BP (F (3, 189)=13.40, p<0.01), heart rate (F (3, 189)=26.28, p<0.01), and respiratory rates (F (3, 189)=5.77, p<0.01) were significantly decreased after back massage. Oxygen saturation levels showed significant increases (F (3, 189)=42.82, p<0.01). Male participants revealed a more significant reduction in anxiety than the female participants (F (1, 50)=7.27, p=0.01). Those with more severe heart failure and greater levels of anxiety (F (2, 61)=4.31, p=0.02) and systolic BP (F (2, 61)=3.86, p=0.03) demonstrated significantly greater responses to back massage. Back massage significantly reduced anxiety in the study population. Systolic BP decreased to a greater degree in the male participants, particularly in those with severe heart failure and greater levels of anxiety and higher systolic BP. This study was conducted without a control group. Randomized clinical trials are needed to validate the effectiveness of back massage on patients with CHF.

An improved label propagation algorithm based on node importance and random walk for community detection

NASA Astrophysics Data System (ADS)

Ma, Tianren; Xia, Zhengyou

2017-05-01

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

Improving back projection imaging with a novel physics-based aftershock calibration approach: A case study of the 2015 Gorkha earthquake

NASA Astrophysics Data System (ADS)

Meng, Lingsen; Zhang, Ailin; Yagi, Yuji

2016-01-01

The 2015 Mw 7.8 Nepal-Gorkha earthquake with casualties of over 9000 people was the most devastating disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Its rupture process was imaged by teleseismic back projections (BP) of seismograms recorded by three, large regional networks in Australia, North America, and Europe. The source images of all three arrays reveal a unilateral eastward rupture; however, the propagation directions and speeds differ significantly between the arrays. To understand the spatial uncertainties of the BP analyses, we analyze four moderate size aftershocks recorded by all three arrays exactly as had been conducted for the main shock. The apparent source locations inferred from BPs are systematically biased from the catalog locations, as a result of a slowness error caused by three-dimensional Earth structures. We introduce a physics-based slowness correction that successfully mitigates the source location discrepancies among the arrays. Our calibrated BPs are found to be mutually consistent and reveal a unilateral rupture propagating eastward at a speed of 2.7 km/s, localized in a relatively narrow and deep swath along the downdip edge of the locked Himalayan thrust zone. We find that the 2015 Gorkha earthquake was a localized rupture that failed to break the entire Himalayan décollement to the surface, which can be regarded as an intermediate event during the interseismic period of larger Himalayan ruptures that break the whole seismogenic zone width. Thus, our physics-based slowness correction is an important technical improvement of BP, mitigating spatial uncertainties and improving the robustness of single and multiarray studies.

Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

NASA Astrophysics Data System (ADS)

Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.

2016-09-01

Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.

A New Method of Synthetic Aperture Radar Image Reconstruction Using Modified Convolution Back-Projection Algorithm.

DTIC Science & Technology

1986-08-01

SECURITY CLASSIFICATION AUTHORITY 3 DISTRIBUTIONAVAILABILITY OF REPORT N/A \\pproved for public release, 21b. OECLASS FI) CAT ) ON/OOWNGRAOING SCMEOLLE...from this set of projections. The Convolution Back-Projection (CBP) algorithm is widely used technique in Computer Aide Tomography ( CAT ). In this work...University of Illinois at Urbana-Champaign. 1985 Ac % DTICEl_ FCTE " AUG 1 11986 Urbana. Illinois U,) I A NEW METHOD OF SYNTHETIC APERTURE RADAR IMAGE

BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

PubMed Central

Pi, Erxu; Mantri, Nitin; Ngai, Sai Ming; Lu, Hongfei; Du, Liqun

2013-01-01

Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, ‘Savannah’ and ‘Princess VII’). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R2 values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of

Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

NASA Astrophysics Data System (ADS)

Vellingiri, Govindaraj; Jayabalan, Ramesh

2018-03-01

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.

Effect of Back Massage Intervention on Anxiety, Comfort, and Physiologic Responses in Patients with Congestive Heart Failure

PubMed Central

Chen, Wei-Ling; Liu, Gin-Jen; Chiang, Ming-Chu; Fu, Mao-Young; Hsieh, Yuan-Kai

2013-01-01

Abstract Background Patients suffering from congestive heart failure (CHF) frequently feel physical suffering and anxiety. Objectives The researchers investigated whether back massage could reduce anxiety, discomfort, and physical suffering in patients with CHF. The effects of gender and severity-dependent response of back massage on anxiety and discomfort in patients were also analyzed. Design The study used a quasi-experimental design with one group pretest and posttest. Participants Sixty-four participants were recruited in southern Taiwan. Outcome measures The modified State Anxiety Inventory, the discomfort Visual Analogue Scale, electronic blood pressure (BP) gauges, stethoscopes and the pulse oximetry were used in this study. Results The participants' systolic BP (F (3, 189)=18.91, p<0.01), diastolic BP (F (3, 189)=13.40, p<0.01), heart rate (F (3, 189)=26.28, p<0.01), and respiratory rates (F (3, 189)=5.77, p<0.01) were significantly decreased after back massage. Oxygen saturation levels showed significant increases (F (3, 189)=42.82, p<0.01). Male participants revealed a more significant reduction in anxiety than the female participants (F (1, 50)=7.27, p=0.01). Those with more severe heart failure and greater levels of anxiety (F (2, 61)=4.31, p=0.02) and systolic BP (F (2, 61)=3.86, p=0.03) demonstrated significantly greater responses to back massage. Conclusions Back massage significantly reduced anxiety in the study population. Systolic BP decreased to a greater degree in the male participants, particularly in those with severe heart failure and greater levels of anxiety and higher systolic BP. This study was conducted without a control group. Randomized clinical trials are needed to validate the effectiveness of back massage on patients with CHF. PMID:23186129

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

Efficient, balanced, transmission line RF circuits by back propagation of common impedance nodes.

PubMed

Markhasin, Evgeny; Hu, Jianping; Su, Yongchao; Herzfeld, Judith; Griffin, Robert G

2013-06-01

We present a new, efficient strategy for designing fully balanced transmission line RF circuits for solid state NMR probes based on back propagation of common impedance nodes (BPCIN). In this approach, the impedance node phenomenon is the sole means of achieving mutual RF isolation and balance in all RF channels. BPCIN is illustrated using a custom double resonance 3.2 mm MAS probe operating at 500 MHz ((1)H) and 125 MHz ((13)C). When fully optimized, the probe is capable of producing high homogeneity (810°/90° ratios of 86% and 89% for (1)H and (13)C, respectively) and high efficiency (γB1=100 kHz for (1)H and (13)C at 70 W and 180 W of RF input, respectively; up to 360 kHz for (1)H). The probe's performance is illustrated by 2D MAS correlation spectra of microcrystals of the tripeptide N-f-MLF-OH and hydrated amyloid fibrils of the protein PI3-SH3. Copyright © 2013 Elsevier Inc. All rights reserved.

PSF estimation for defocus blurred image based on quantum back-propagation neural network

NASA Astrophysics Data System (ADS)

Gao, Kun; Zhang, Yan; Shao, Xiao-guang; Liu, Ying-hui; Ni, Guoqiang

2010-11-01

Images obtained by an aberration-free system are defocused blur due to motion in depth and/or zooming. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. But it is difficult to identify the analytic model of PSF precisely due to the complexity of the degradation process. Inspired by the similarity between the quantum process and imaging process in the probability and statistics fields, one reformed multilayer quantum neural network (QNN) is proposed to estimate PSF of the defocus blurred image. Different from the conventional artificial neural network (ANN), an improved quantum neuron model is used in the hidden layer instead, which introduces a 2-bit controlled NOT quantum gate to control output and adopts 2 texture and edge features as the input vectors. The supervised back-propagation learning rule is adopted to train network based on training sets from the historical images. Test results show that this method owns excellent features of high precision and strong generalization ability.

Neural Network Back-Propagation Algorithm for Sensing Hypergols

NASA Technical Reports Server (NTRS)

Perotti, Jose; Lewis, Mark; Medelius, Pedro; Bastin, Gary

2013-01-01

Fast, continuous detection of a wide range of hazardous substances simultaneously is needed to achieve improved safety for personnel working with hypergolic fuels and oxidizers, as well as other hazardous substances, with a requirement for such detection systems to warn personnel immediately upon the sudden advent of hazardous conditions, with a high probability of detection and a low false alarm rate. The primary purpose of this software is to read the voltage outputs from voltage dividers containing carbon nano - tube sensors as a variable resistance leg, and to recognize quickly when a leak has occurred through recognizing that a generalized pattern change in resistivity of a carbon nanotube sensor has occurred upon exposure to dangerous substances, and, further, to identify quickly just what substance is present through detailed pattern recognition of the shape of the response provided by the carbon nanotube sensor.

Limited angle C-arm tomosynthesis reconstruction algorithms

NASA Astrophysics Data System (ADS)

Malalla, Nuhad A. Y.; Xu, Shiyu; Chen, Ying

2015-03-01

In this paper, C-arm tomosynthesis with digital detector was investigated as a novel three dimensional (3D) imaging technique. Digital tomosythses is an imaging technique to provide 3D information of the object by reconstructing slices passing through the object, based on a series of angular projection views with respect to the object. C-arm tomosynthesis provides two dimensional (2D) X-ray projection images with rotation (-/+20 angular range) of both X-ray source and detector. In this paper, four representative reconstruction algorithms including point by point back projection (BP), filtered back projection (FBP), simultaneous algebraic reconstruction technique (SART) and maximum likelihood expectation maximization (MLEM) were investigated. Dataset of 25 projection views of 3D spherical object that located at center of C-arm imaging space was simulated from 25 angular locations over a total view angle of 40 degrees. With reconstructed images, 3D mesh plot and 2D line profile of normalized pixel intensities on focus reconstruction plane crossing the center of the object were studied with each reconstruction algorithm. Results demonstrated the capability to generate 3D information from limited angle C-arm tomosynthesis. Since C-arm tomosynthesis is relatively compact, portable and can avoid moving patients, it has been investigated for different clinical applications ranging from tumor surgery to interventional radiology. It is very important to evaluate C-arm tomosynthesis for valuable applications.

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Trunk Muscle Activity during Drop Jump Performance in Adolescent Athletes with Back Pain.

PubMed

Mueller, Steffen; Stoll, Josefine; Mueller, Juliane; Cassel, Michael; Mayer, Frank

2017-01-01

In the context of back pain, great emphasis has been placed on the importance of trunk stability, especially in situations requiring compensation of repetitive, intense loading induced during high-performance activities, e.g., jumping or landing. This study aims to evaluate trunk muscle activity during drop jump in adolescent athletes with back pain (BP) compared to athletes without back pain (NBP). Eleven adolescent athletes suffering back pain (BP: m/f: n = 4/7; 15.9 ± 1.3 y; 176 ± 11 cm; 68 ± 11 kg; 12.4 ± 10.5 h/we training) and 11 matched athletes without back pain (NBP: m/f: n = 4/7; 15.5 ± 1.3 y; 174 ± 7 cm; 67 ± 8 kg; 14.9 ± 9.5 h/we training) were evaluated. Subjects conducted 3 drop jumps onto a force plate (ground reaction force). Bilateral 12-lead SEMG (surface Electromyography) was applied to assess trunk muscle activity. Ground contact time [ms], maximum vertical jump force [N], jump time [ms] and the jump performance index [m/s] were calculated for drop jumps. SEMG amplitudes (RMS: root mean square [%]) for all 12 single muscles were normalized to MIVC (maximum isometric voluntary contraction) and analyzed in 4 time windows (100 ms pre- and 200 ms post-initial ground contact, 100 ms pre- and 200 ms post-landing) as outcome variables. In addition, muscles were grouped and analyzed in ventral and dorsal muscles, as well as straight and transverse trunk muscles. Drop jump ground reaction force variables did not differ between NBP and BP ( p > 0.05). Mm obliquus externus and internus abdominis presented higher SEMG amplitudes (1.3-1.9-fold) for BP ( p < 0.05). Mm rectus abdominis, erector spinae thoracic/lumbar and latissimus dorsi did not differ ( p > 0.05). The muscle group analysis over the whole jumping cycle showed statistically significantly higher SEMG amplitudes for BP in the ventral ( p = 0.031) and transverse muscles ( p = 0.020) compared to NBP. Higher activity of transverse, but not straight, trunk muscles might indicate a specific

An HMGA2-IGF2BP2 Axis Regulates Myoblast Proliferation and Myogenesis

PubMed Central

Li, Zhizhong; Gilbert, Jason A.; Zhang, Yunyu; Zhang, Minsi; Qiu, Qiong; Ramanujan, Krishnan; Shavlakadze, Tea; Eash, John K.; Scaramozza, Annarita; Goddeeris, Matthew M.; Kirsch, David G.; Campbell, Kevin P.; Brack, Andrew S.; Glass, David J.

2013-01-01

Summary A group of genes that are highly and specifically expressed in proliferating skeletal myoblasts during myogenesis was identified. Expression of one of these genes, Hmga2, increases coincident with satellite cell activation, and later its expression significantly declines correlating with fusion of myoblasts into myotubes. Hmga2 knockout mice exhibit impaired muscle development and reduced myoblast proliferation, while overexpression of HMGA2 promotes myoblast growth. This perturbation in proliferation can be explained by the finding that HMGA2 directly regulates the RNA-binding protein IGF2BP2. Add-back of IGF2BP2 rescues the phenotype. IGF2BP2 in turn binds to and controls the translation of a set of mRNAs, including c-myc, Sp1, and Igf1r. These data demonstrate that the HMGA2-IGF2BP2 axis functions as a key regulator of satellite cell activation and therefore skeletal muscle development. PMID:23177649

An HMGA2-IGF2BP2 axis regulates myoblast proliferation and myogenesis.

PubMed

Li, Zhizhong; Gilbert, Jason A; Zhang, Yunyu; Zhang, Minsi; Qiu, Qiong; Ramanujan, Krishnan; Shavlakadze, Tea; Eash, John K; Scaramozza, Annarita; Goddeeris, Matthew M; Kirsch, David G; Campbell, Kevin P; Brack, Andrew S; Glass, David J

2012-12-11

A group of genes that are highly and specifically expressed in proliferating skeletal myoblasts during myogenesis was identified. Expression of one of these genes, Hmga2, increases coincident with satellite cell activation, and later its expression significantly declines correlating with fusion of myoblasts into myotubes. Hmga2 knockout mice exhibit impaired muscle development and reduced myoblast proliferation, while overexpression of HMGA2 promotes myoblast growth. This perturbation in proliferation can be explained by the finding that HMGA2 directly regulates the RNA-binding protein IGF2BP2. Add-back of IGF2BP2 rescues the phenotype. IGF2BP2 in turn binds to and controls the translation of a set of mRNAs, including c-myc, Sp1, and Igf1r. These data demonstrate that the HMGA2-IGF2BP2 axis functions as a key regulator of satellite cell activation and therefore skeletal muscle development. Copyright © 2012 Elsevier Inc. All rights reserved.

Network propagation in the cytoscape cyberinfrastructure.

PubMed

Carlin, Daniel E; Demchak, Barry; Pratt, Dexter; Sage, Eric; Ideker, Trey

2017-10-01

Network propagation is an important and widely used algorithm in systems biology, with applications in protein function prediction, disease gene prioritization, and patient stratification. However, up to this point it has required significant expertise to run. Here we extend the popular network analysis program Cytoscape to perform network propagation as an integrated function. Such integration greatly increases the access to network propagation by putting it in the hands of biologists and linking it to the many other types of network analysis and visualization available through Cytoscape. We demonstrate the power and utility of the algorithm by identifying mutations conferring resistance to Vemurafenib.

Accurate Finite Difference Algorithms

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1996-01-01

Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

PubMed

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low

Typing SNP based on the near-infrared spectroscopy and artificial neural network

NASA Astrophysics Data System (ADS)

Ren, Li; Wang, Wei-Peng; Gao, Yu-Zhen; Yu, Xiao-Wei; Xie, Hong-Ping

2009-07-01

Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G > A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.

The Propagation of Cosmic Rays from the Galactic Wind Termination Shock: Back to the Galaxy?

NASA Astrophysics Data System (ADS)

Merten, Lukas; Bustard, Chad; Zweibel, Ellen G.; Becker Tjus, Julia

2018-05-01

Although several theories exist for the origin of cosmic rays (CRs) in the region between the spectral “knee” and “ankle,” this problem is still unsolved. A variety of observations suggest that the transition from Galactic to extragalactic sources occurs in this energy range. In this work, we examine whether a Galactic wind that eventually forms a termination shock far outside the Galactic plane can contribute as a possible source to the observed flux in the region of interest. Previous work by Bustard et al. estimated that particles can be accelerated to energies above the “knee” up to R max = 1016 eV for parameters drawn from a model of a Milky Way wind. A remaining question is whether the accelerated CRs can propagate back into the Galaxy. To answer this crucial question, we simulate the propagation of the CRs using the low-energy extension of the CRPropa framework, based on the solution of the transport equation via stochastic differential equations. The setup includes all relevant processes, including three-dimensional anisotropic spatial diffusion, advection, and corresponding adiabatic cooling. We find that, assuming realistic parameters for the shock evolution, a possible Galactic termination shock can contribute significantly to the energy budget in the “knee” region and above. We estimate the resulting produced neutrino fluxes and find them to be below measurements from IceCube and limits by KM3NeT.

Application of the GA-BP Neural Network in Earthwork Calculation

NASA Astrophysics Data System (ADS)

Fang, Peng; Cai, Zhixiong; Zhang, Ping

2018-01-01

The calculation of earthwork quantity is the key factor to determine the project cost estimate and the optimization of the scheme. It is of great significance and function in the excavation of earth and rock works. We use optimization principle of GA-BP intelligent algorithm running process, and on the basis of earthwork quantity and cost information database, the design of the GA-BP neural network intelligent computing model, through the network training and learning, the accuracy of the results meet the actual engineering construction of gauge fan requirements, it provides a new approach for other projects the calculation, and has good popularization value.

Paracousti-UQ: A Stochastic 3-D Acoustic Wave Propagation Algorithm.

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

Preston, Leiph

Acoustic full waveform algorithms, such as Paracousti, provide deterministic solutions in complex, 3-D variable environments. In reality, environmental and source characteristics are often only known in a statistical sense. Thus, to fully characterize the expected sound levels within an environment, this uncertainty in environmental and source factors should be incorporated into the acoustic simulations. Performing Monte Carlo (MC) simulations is one method of assessing this uncertainty, but it can quickly become computationally intractable for realistic problems. An alternative method, using the technique of stochastic partial differential equations (SPDE), allows computation of the statistical properties of output signals at a fractionmore » of the computational cost of MC. Paracousti-UQ solves the SPDE system of 3-D acoustic wave propagation equations and provides estimates of the uncertainty of the output simulated wave field (e.g., amplitudes, waveforms) based on estimated probability distributions of the input medium and source parameters. This report describes the derivation of the stochastic partial differential equations, their implementation, and comparison of Paracousti-UQ results with MC simulations using simple models.« less

Parallel Splash Belief Propagation

DTIC Science & Technology

2010-08-01

s/ ROGER J. DZIEGIEL, Jr. MICHAEL J. WESSING, Deputy Chief Work Unit Manager For... Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE...Propagation algorithm outperforms synchronous, round-robin, wild-fire ( Ranganathan et al., 2007), and residual (Elidan et al., 2006) belief propagation

Multi-exemplar affinity propagation.

PubMed

Wang, Chang-Dong; Lai, Jian-Huang; Suen, Ching Y; Zhu, Jun-Yong

2013-09-01

The affinity propagation (AP) clustering algorithm has received much attention in the past few years. AP is appealing because it is efficient, insensitive to initialization, and it produces clusters at a lower error rate than other exemplar-based methods. However, its single-exemplar model becomes inadequate when applied to model multisubclasses in some situations such as scene analysis and character recognition. To remedy this deficiency, we have extended the single-exemplar model to a multi-exemplar one to create a new multi-exemplar affinity propagation (MEAP) algorithm. This new model automatically determines the number of exemplars in each cluster associated with a super exemplar to approximate the subclasses in the category. Solving the model is NP-hard and we tackle it with the max-sum belief propagation to produce neighborhood maximum clusters, with no need to specify beforehand the number of clusters, multi-exemplars, and superexemplars. Also, utilizing the sparsity in the data, we are able to reduce substantially the computational time and storage. Experimental studies have shown MEAP's significant improvements over other algorithms on unsupervised image categorization and the clustering of handwritten digits.

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

Using Elman recurrent neural networks with conjugate gradient algorithm in determining the anesthetic the amount of anesthetic medicine to be applied.

PubMed

Güntürkün, Rüştü

2010-08-01

In this study, Elman recurrent neural networks have been defined by using conjugate gradient algorithm in order to determine the depth of anesthesia in the continuation stage of the anesthesia and to estimate the amount of medicine to be applied at that moment. The feed forward neural networks are also used for comparison. The conjugate gradient algorithm is compared with back propagation (BP) for training of the neural Networks. The applied artificial neural network is composed of three layers, namely the input layer, the hidden layer and the output layer. The nonlinear activation function sigmoid (sigmoid function) has been used in the hidden layer and the output layer. EEG data has been recorded with Nihon Kohden 9200 brand 22-channel EEG device. The international 8-channel bipolar 10-20 montage system (8 TB-b system) has been used in assembling the recording electrodes. EEG data have been recorded by being sampled once in every 2 milliseconds. The artificial neural network has been designed so as to have 60 neurons in the input layer, 30 neurons in the hidden layer and 1 neuron in the output layer. The values of the power spectral density (PSD) of 10-second EEG segments which correspond to the 1-50 Hz frequency range; the ratio of the total power of PSD values of the EEG segment at that moment in the same range to the total of PSD values of EEG segment taken prior to the anesthesia.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

PubMed

Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira; Heo, Min-Suk

2013-09-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

GPU Accelerated Ultrasonic Tomography Using Propagation and Back Propagation Method

DTIC Science & Technology

2015-09-28

the medical imaging field using GPUs has been done for many years. In [1], Copeland et al. used 2D images , obtained by X - ray projections, to...Index Terms— Medical Imaging , Ultrasonic Tomography, GPU, CUDA, Parallel Computing I. INTRODUCTION GRAPHIC Processing Units (GPUs) are computation... Imaging Algorithm The process of reconstructing images from ultrasonic infor- mation starts with the following acoustical wave equation: ∂2 ∂t2 u ( x

BP fusion model for the detection of oil spills on the sea by remote sensing

NASA Astrophysics Data System (ADS)

Chen, Weiwei; An, Jubai; Zhang, Hande; Lin, Bin

2003-06-01

Oil spills are very serious marine pollution in many countries. In order to detect and identify the oil-spilled on the sea by remote sensor, scientists have to conduct a research work on the remote sensing image. As to the detection of oil spills on the sea, edge detection is an important technology in image processing. There are many algorithms of edge detection developed for image processing. These edge detection algorithms always have their own advantages and disadvantages in the image processing. Based on the primary requirements of edge detection of the oil spills" image on the sea, computation time and detection accuracy, we developed a fusion model. The model employed a BP neural net to fuse the detection results of simple operators. The reason we selected BP neural net as the fusion technology is that the relation between simple operators" result of edge gray level and the image"s true edge gray level is nonlinear, while BP neural net is good at solving the nonlinear identification problem. Therefore in this paper we trained a BP neural net by some oil spill images, then applied the BP fusion model on the edge detection of other oil spill images and obtained a good result. In this paper the detection result of some gradient operators and Laplacian operator are also compared with the result of BP fusion model to analysis the fusion effect. At last the paper pointed out that the fusion model has higher accuracy and higher speed in the processing oil spill image"s edge detection.

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Propagating Qualitative Values Through Quantitative Equations

NASA Technical Reports Server (NTRS)

Kulkarni, Deepak

1992-01-01

In most practical problems where traditional numeric simulation is not adequate, one need to reason about a system with both qualitative and quantitative equations. In this paper, we address the problem of propagating qualitative values represented as interval values through quantitative equations. Previous research has produced exponential-time algorithms for approximate solution of the problem. These may not meet the stringent requirements of many real time applications. This paper advances the state of art by producing a linear-time algorithm that can propagate a qualitative value through a class of complex quantitative equations exactly and through arbitrary algebraic expressions approximately. The algorithm was found applicable to Space Shuttle Reaction Control System model.

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

PubMed

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Numerical Algorithms for Precise and Efficient Orbit Propagation and Positioning

NASA Astrophysics Data System (ADS)

Bradley, Ben K.

Motivated by the growing space catalog and the demands for precise orbit determination with shorter latency for science and reconnaissance missions, this research improves the computational performance of orbit propagation through more efficient and precise numerical integration and frame transformation implementations. Propagation of satellite orbits is required for astrodynamics applications including mission design, orbit determination in support of operations and payload data analysis, and conjunction assessment. Each of these applications has somewhat different requirements in terms of accuracy, precision, latency, and computational load. This dissertation develops procedures to achieve various levels of accuracy while minimizing computational cost for diverse orbit determination applications. This is done by addressing two aspects of orbit determination: (1) numerical integration used for orbit propagation and (2) precise frame transformations necessary for force model evaluation and station coordinate rotations. This dissertation describes a recently developed method for numerical integration, dubbed Bandlimited Collocation Implicit Runge-Kutta (BLC-IRK), and compare its efficiency in propagating orbits to existing techniques commonly used in astrodynamics. The BLC-IRK scheme uses generalized Gaussian quadratures for bandlimited functions. It requires significantly fewer force function evaluations than explicit Runge-Kutta schemes and approaches the efficiency of the 8th-order Gauss-Jackson multistep method. Converting between the Geocentric Celestial Reference System (GCRS) and International Terrestrial Reference System (ITRS) is necessary for many applications in astrodynamics, such as orbit propagation, orbit determination, and analyzing geoscience data from satellite missions. This dissertation provides simplifications to the Celestial Intermediate Origin (CIO) transformation scheme and Earth orientation parameter (EOP) storage for use in positioning and

Spectrally Shaped DP-16QAM Super-Channel Transmission with Multi-Channel Digital Back-Propagation

PubMed Central

Maher, Robert; Xu, Tianhua; Galdino, Lidia; Sato, Masaki; Alvarado, Alex; Shi, Kai; Savory, Seb J.; Thomsen, Benn C.; Killey, Robert I.; Bayvel, Polina

2015-01-01

The achievable transmission capacity of conventional optical fibre communication systems is limited by nonlinear distortions due to the Kerr effect and the difficulty in modulating the optical field to effectively use the available fibre bandwidth. In order to achieve a high information spectral density (ISD), while simultaneously maintaining transmission reach, multi-channel fibre nonlinearity compensation and spectrally efficient data encoding must be utilised. In this work, we use a single coherent super-receiver to simultaneously receive a DP-16QAM super-channel, consisting of seven spectrally shaped 10GBd sub-carriers spaced at the Nyquist frequency. Effective nonlinearity mitigation is achieved using multi-channel digital back-propagation (MC-DBP) and this technique is combined with an optimised forward error correction implementation to demonstrate a record gain in transmission reach of 85%; increasing the maximum transmission distance from 3190 km to 5890 km, with an ISD of 6.60 b/s/Hz. In addition, this report outlines for the first time, the sensitivity of MC-DBP gain to linear transmission line impairments and defines a trade-off between performance and complexity. PMID:25645457

Evaluation of a treatment-based classification algorithm for low back pain: a cross-sectional study.

PubMed

Stanton, Tasha R; Fritz, Julie M; Hancock, Mark J; Latimer, Jane; Maher, Christopher G; Wand, Benedict M; Parent, Eric C

2011-04-01

Several studies have investigated criteria for classifying patients with low back pain (LBP) into treatment-based subgroups. A comprehensive algorithm was created to translate these criteria into a clinical decision-making guide. This study investigated the translation of the individual subgroup criteria into a comprehensive algorithm by studying the prevalence of patients meeting the criteria for each treatment subgroup and the reliability of the classification. This was a cross-sectional, observational study. Two hundred fifty patients with acute or subacute LBP were recruited from the United States and Australia to participate in the study. Trained physical therapists performed standardized assessments on all participants. The researchers used these findings to classify participants into subgroups. Thirty-one participants were reassessed to determine interrater reliability of the algorithm decision. Based on individual subgroup criteria, 25.2% (95% confidence interval [CI]=19.8%-30.6%) of the participants did not meet the criteria for any subgroup, 49.6% (95% CI=43.4%-55.8%) of the participants met the criteria for only one subgroup, and 25.2% (95% CI=19.8%-30.6%) of the participants met the criteria for more than one subgroup. The most common combination of subgroups was manipulation + specific exercise (68.4% of the participants who met the criteria for 2 subgroups). Reliability of the algorithm decision was moderate (kappa=0.52, 95% CI=0.27-0.77, percentage of agreement=67%). Due to a relatively small patient sample, reliability estimates are somewhat imprecise. These findings provide important clinical data to guide future research and revisions to the algorithm. The finding that 25% of the participants met the criteria for more than one subgroup has important implications for the sequencing of treatments in the algorithm. Likewise, the finding that 25% of the participants did not meet the criteria for any subgroup provides important information regarding

TopBP1 functions with 53BP1 in the G1 DNA damage checkpoint

PubMed Central

Cescutti, Rachele; Negrini, Simona; Kohzaki, Masaoki; Halazonetis, Thanos D

2010-01-01

TopBP1 is a checkpoint protein that colocalizes with ATR at sites of DNA replication stress. In this study, we show that TopBP1 also colocalizes with 53BP1 at sites of DNA double-strand breaks (DSBs), but only in the G1-phase of the cell cycle. Recruitment of TopBP1 to sites of DNA replication stress was dependent on BRCT domains 1–2 and 7–8, whereas recruitment to sites of DNA DSBs was dependent on BRCT domains 1–2 and 4–5. The BRCT domains 4–5 interacted with 53BP1 and recruitment of TopBP1 to sites of DNA DSBs in G1 was dependent on 53BP1. As TopBP1 contains a domain important for ATR activation, we examined whether it contributes to the G1 cell cycle checkpoint. By monitoring the entry of irradiated G1 cells into S-phase, we observed a checkpoint defect after siRNA-mediated depletion of TopBP1, 53BP1 or ATM. Thus, TopBP1 may mediate the checkpoint function of 53BP1 in G1. PMID:20871591

TopBP1 functions with 53BP1 in the G1 DNA damage checkpoint.

PubMed

Cescutti, Rachele; Negrini, Simona; Kohzaki, Masaoki; Halazonetis, Thanos D

2010-11-03

TopBP1 is a checkpoint protein that colocalizes with ATR at sites of DNA replication stress. In this study, we show that TopBP1 also colocalizes with 53BP1 at sites of DNA double-strand breaks (DSBs), but only in the G1-phase of the cell cycle. Recruitment of TopBP1 to sites of DNA replication stress was dependent on BRCT domains 1-2 and 7-8, whereas recruitment to sites of DNA DSBs was dependent on BRCT domains 1-2 and 4-5. The BRCT domains 4-5 interacted with 53BP1 and recruitment of TopBP1 to sites of DNA DSBs in G1 was dependent on 53BP1. As TopBP1 contains a domain important for ATR activation, we examined whether it contributes to the G1 cell cycle checkpoint. By monitoring the entry of irradiated G1 cells into S-phase, we observed a checkpoint defect after siRNA-mediated depletion of TopBP1, 53BP1 or ATM. Thus, TopBP1 may mediate the checkpoint function of 53BP1 in G1.

Steps toward quantitative infrasound propagation modeling

NASA Astrophysics Data System (ADS)

Waxler, Roger; Assink, Jelle; Lalande, Jean-Marie; Velea, Doru

2016-04-01

Realistic propagation modeling requires propagation models capable of incorporating the relevant physical phenomena as well as sufficiently accurate atmospheric specifications. The wind speed and temperature gradients in the atmosphere provide multiple ducts in which low frequency sound, infrasound, can propagate efficiently. The winds in the atmosphere are quite variable, both temporally and spatially, causing the sound ducts to fluctuate. For ground to ground propagation the ducts can be borderline in that small perturbations can create or destroy a duct. In such cases the signal propagation is very sensitive to fluctuations in the wind, often producing highly dispersed signals. The accuracy of atmospheric specifications is constantly improving as sounding technology develops. There is, however, a disconnect between sound propagation and atmospheric specification in that atmospheric specifications are necessarily statistical in nature while sound propagates through a particular atmospheric state. In addition infrasonic signals can travel to great altitudes, on the order of 120 km, before refracting back to earth. At such altitudes the atmosphere becomes quite rare causing sound propagation to become highly non-linear and attenuating. Approaches to these problems will be presented.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

PubMed Central

Vázquez, Roberto A.

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

Effect of Backing Plate Thermal Property on Friction Stir Welding of 25-mm-Thick AA6061

NASA Astrophysics Data System (ADS)

Upadhyay, Piyush; Reynolds, Anthony

2014-04-01

By using backing plates made out of materials with widely varying thermal diffusivity this work seeks to elucidate the effects of the root side thermal boundary condition on weld process variables and resulting joint properties. Welds were made in 25.4-mm-thick AA6061 using ceramic, titanium, steel, and aluminum as backing plate (BP) material. Welds were also made using a "composite backing plate" consisting of longitudinal narrow strip of low diffusivity material at the center and two side plates of high diffusivity aluminum. Stir zone temperature during the welding was measured using two thermocouples spot welded at the core of the probe: one at the midplane height and another near the tip of the probe corresponding to the root of the weld. Steady state midplane probe temperatures for all the BPs used were found to be very similar. Near root peak temperature, however, varied significantly among weld made with different BPs all other things being equal. Whereas the near root and midplane temperature were the same in the case of ceramic backing plate, the root peak temperature was 318 K (45 °C) less than the midplane temperature in the case of aluminum BP. The trends of nugget hardness and grain size in through thickness direction were in agreement with the measured probe temperatures. Hardness and tensile test results show that the use of composite BP results in stronger joint compared to monolithic steel BP.

Application of a hybrid model of neural networks and genetic algorithms to evaluate landslide susceptibility

NASA Astrophysics Data System (ADS)

Wang, H. B.; Li, J. W.; Zhou, B.; Yuan, Z. Q.; Chen, Y. P.

2013-03-01

In the last few decades, the development of Geographical Information Systems (GIS) technology has provided a method for the evaluation of landslide susceptibility and hazard. Slope units were found to be appropriate for the fundamental morphological elements in landslide susceptibility evaluation. Following the DEM construction in a loess area susceptible to landslides, the direct-reverse DEM technology was employed to generate 216 slope units in the studied area. After a detailed investigation, the landslide inventory was mapped in which 39 landslides, including paleo-landslides, old landslides and recent landslides, were present. Of the 216 slope units, 123 involved landslides. To analyze the mechanism of these landslides, six environmental factors were selected to evaluate landslide occurrence: slope angle, aspect, the height and shape of the slope, distance to river and human activities. These factors were extracted in terms of the slope unit within the ArcGIS software. The spatial analysis demonstrates that most of the landslides are located on convex slopes at an elevation of 100-150 m with slope angles from 135°-225° and 40°-60°. Landslide occurrence was then checked according to these environmental factors using an artificial neural network with back propagation, optimized by genetic algorithms. A dataset of 120 slope units was chosen for training the neural network model, i.e., 80 units with landslide presence and 40 units without landslide presence. The parameters of genetic algorithms and neural networks were then set: population size of 100, crossover probability of 0.65, mutation probability of 0.01, momentum factor of 0.60, learning rate of 0.7, max learning number of 10 000, and target error of 0.000001. After training on the datasets, the susceptibility of landslides was mapped for the land-use plan and hazard mitigation. Comparing the susceptibility map with landslide inventory, it was noted that the prediction accuracy of landslide occurrence

Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines

PubMed Central

Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel

2013-01-01

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562

Application of Gaussian beam ray-equivalent model and back-propagation artificial neural network in laser diode fast axis collimator assembly.

PubMed

Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido

2016-08-10

The paper presents the development of a tool based on a back-propagation artificial neural network to assist in the accurate positioning of the lenses used to collimate the beam from semiconductor laser diodes along the so-called fast axis. After training using a Gaussian beam ray-equivalent model, the network is capable of indicating the tilt, decenter, and defocus of such lenses from the measured field distribution, so the operator can determine the errors with respect to the actual lens position and optimize the diode assembly procedure. An experimental validation using a typical configuration exploited in multi-emitter diode module assembly and fast axis collimating lenses with different focal lengths and numerical apertures is reported.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

A Configurational-Bias-Monte-Carlo Back-Mapping Algorithm for Efficient and Rapid Conversion of Coarse-Grained Water Structures Into Atomistic Models.

PubMed

Loeffler, Troy David; Chan, Henry; Narayanan, Badri; Cherukara, Mathew J; Gray, Stephen K; Sankaranarayanan, Subramanian K R S

2018-06-20

Coarse-grained molecular dynamics (MD) simulations represent a powerful approach to simulate longer time scale and larger length scale phenomena than those accessible to all-atom models. The gain in efficiency, however, comes at the cost of atomistic details. The reverse transformation, also known as back-mapping, of coarse grained beads into their atomistic constituents represents a major challenge. Most existing approaches are limited to specific molecules or specific force-fields and often rely on running a long time atomistic MD of the back-mapped configuration to arrive at an optimal solution. Such approaches are problematic when dealing with systems with high diffusion barriers. Here, we introduce a new extension of the configurational-bias-Monte-Carlo (CBMC) algorithm, which we term the crystalline-configurational-bias-Monte-Carlo (C-CBMC) algortihm, that allows rapid and efficient conversion of a coarse-grained model back into its atomistic representation. Although the method is generic, we use a coarse-grained water model as a representative example and demonstrate the back-mapping or reverse transformation for model systems ranging from the ice-liquid water interface to amorphous and crystalline ice configurations. A series of simulations using the TIP4P/Ice model are performed to compare the new CBMC method to several other standard Monte Carlo and Molecular Dynamics based back-mapping techniques. In all the cases, the C-CBMC algorithm is able to find optimal hydrogen bonded configuration many thousand evaluations/steps sooner than the other methods compared within this paper. For crystalline ice structures such as a hexagonal, cubic, and cubic-hexagonal stacking disorder structures, the C-CBMC was able to find structures that were between 0.05 and 0.1 eV/water molecule lower in energy than the ground state energies predicted by the other methods. Detailed analysis of the atomistic structures show a significantly better global hydrogen positioning when

Imaging tilted transversely isotropic media with a generalised screen propagator

NASA Astrophysics Data System (ADS)

Shin, Sung-Il; Byun, Joongmoo; Seol, Soon Jee

2015-01-01

One-way wave equation migration is computationally efficient compared with reverse time migration, and it provides a better subsurface image than ray-based migration algorithms when imaging complex structures. Among many one-way wave-based migration algorithms, we adopted the generalised screen propagator (GSP) to build the migration algorithm. When the wavefield propagates through the large velocity variation in lateral or steeply dipping structures, GSP increases the accuracy of the wavefield in wide angle by adopting higher-order terms induced from expansion of the vertical slowness in Taylor series with each perturbation term. To apply the migration algorithm to a more realistic geological structure, we considered tilted transversely isotropic (TTI) media. The new GSP, which contains the tilting angle as a symmetric axis of the anisotropic media, was derived by modifying the GSP designed for vertical transversely isotropic (VTI) media. To verify the developed TTI-GSP, we analysed the accuracy of wave propagation, especially for the new perturbation parameters and the tilting angle; the results clearly showed that the perturbation term of the tilting angle in TTI media has considerable effects on proper propagation. In addition, through numerical tests, we demonstrated that the developed TTI-GS migration algorithm could successfully image a steeply dipping salt flank with high velocity variation around anisotropic layers.

Quantum Graphical Models and Belief Propagation

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

Leifer, M.S.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo Ont., N2L 2Y5; Poulin, D.

Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large numbers of random variables. This paper presents a generalization of these concepts and methods to the quantum case, based on the idea that quantum theory can be thought of as a noncommutative, operator-valued, generalization of classical probability theory. Some novel characterizations of quantum conditional independence are derived, and definitions of Quantum n-Bifactor Networks, Markov Networks, Factor Graphs and Bayesian Networks are proposed. The structure of Quantum Markovmore » Networks is investigated and some partial characterization results are obtained, along the lines of the Hammersley-Clifford theorem. A Quantum Belief Propagation algorithm is presented and is shown to converge on 1-Bifactor Networks and Markov Networks when the underlying graph is a tree. The use of Quantum Belief Propagation as a heuristic algorithm in cases where it is not known to converge is discussed. Applications to decoding quantum error correcting codes and to the simulation of many-body quantum systems are described.« less

Coherent field propagation between tilted planes.

PubMed

Stock, Johannes; Worku, Norman Girma; Gross, Herbert

2017-10-01

Propagating electromagnetic light fields between nonparallel planes is of special importance, e.g., within the design of novel computer-generated holograms or the simulation of optical systems. In contrast to the extensively discussed evaluation between parallel planes, the diffraction-based propagation of light onto a tilted plane is more burdensome, since discrete fast Fourier transforms cannot be applied directly. In this work, we propose a quasi-fast algorithm (O(N 3  log N)) that deals with this problem. Based on a proper decomposition into three rotations, the vectorial field distribution is calculated on a tilted plane using the spectrum of plane waves. The algorithm works on equidistant grids, so neither nonuniform Fourier transforms nor an explicit complex interpolation is necessary. The proposed algorithm is discussed in detail and applied to several examples of practical interest.

GENERAL: Application of Symplectic Algebraic Dynamics Algorithm to Circular Restricted Three-Body Problem

NASA Astrophysics Data System (ADS)

Lu, Wei-Tao; Zhang, Hua; Wang, Shun-Jin

2008-07-01

Symplectic algebraic dynamics algorithm (SADA) for ordinary differential equations is applied to solve numerically the circular restricted three-body problem (CR3BP) in dynamical astronomy for both stable motion and chaotic motion. The result is compared with those of Runge-Kutta algorithm and symplectic algorithm under the fourth order, which shows that SADA has higher accuracy than the others in the long-term calculations of the CR3BP.

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

NASA Astrophysics Data System (ADS)

Sun, Huimin; Meng, Yaoyong; Zhang, Pingli; Li, Yajing; Li, Nan; Li, Caiyun; Guo, Zhiyou

2017-09-01

The age determination of bloodstains is an important and immediate challenge for forensic science. No reliable methods are currently available for estimating the age of bloodstains. Here we report a method for determining the age of bloodstains at different storage temperatures. Bloodstains were stored at 37 °C, 25 °C, 4 °C, and  -20 °C for 80 d. Bloodstains were measured using Raman spectroscopy at various time points. The principal component and a back propagation artificial neural network model were then established for estimating the age of the bloodstains. The results were ideal; the square of correlation coefficient was up to 0.99 (R 2  >  0.99) and the root mean square error of the prediction at lowest reached 55.9829 h. This method is real-time, non-invasive, non-destructive and highly efficiency. It may well prove that Raman spectroscopy is a promising tool for the estimation of the age of bloodstains.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

PubMed

Danaee, Padideh; Rouches, Mason; Wiley, Michelle; Deng, Dezhong; Huang, Liang; Hendrix, David

2018-05-09

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Catalytic Ignition and Upstream Reaction Propagation in Monolith Reactors

NASA Technical Reports Server (NTRS)

Struk, Peter M.; Dietrich, Daniel L.; Miller, Fletcher J.; T'ien, James S.

2007-01-01

Using numerical simulations, this work demonstrates a concept called back-end ignition for lighting-off and pre-heating a catalytic monolith in a power generation system. In this concept, a downstream heat source (e.g. a flame) or resistive heating in the downstream portion of the monolith initiates a localized catalytic reaction which subsequently propagates upstream and heats the entire monolith. The simulations used a transient numerical model of a single catalytic channel which characterizes the behavior of the entire monolith. The model treats both the gas and solid phases and includes detailed homogeneous and heterogeneous reactions. An important parameter in the model for back-end ignition is upstream heat conduction along the solid. The simulations used both dry and wet CO chemistry as a model fuel for the proof-of-concept calculations; the presence of water vapor can trigger homogenous reactions, provided that gas-phase temperatures are adequately high and there is sufficient fuel remaining after surface reactions. With sufficiently high inlet equivalence ratio, back-end ignition occurs using the thermophysical properties of both a ceramic and metal monolith (coated with platinum in both cases), with the heat-up times significantly faster for the metal monolith. For lower equivalence ratios, back-end ignition occurs without upstream propagation. Once light-off and propagation occur, the inlet equivalence ratio could be reduced significantly while still maintaining an ignited monolith as demonstrated by calculations using complete monolith heating.

Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks.

PubMed

Abdollahi, Yadollah; Sairi, Nor Asrina; Said, Suhana Binti Mohd; Abouzari-lotf, Ebrahim; Zakaria, Azmi; Sabri, Mohd Faizul Bin Mohd; Islam, Aminul; Alias, Yatimah

2015-11-05

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up. Copyright © 2015 Elsevier B.V. All rights reserved.

Parallel Clustering Algorithm for Large-Scale Biological Data Sets

PubMed Central

Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

2014-01-01

Backgrounds Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Methods Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. Result A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies. PMID:24705246

Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics-Monte Carlo propagator

NASA Astrophysics Data System (ADS)

Suh, Donghyuk; Radak, Brian K.; Chipot, Christophe; Roux, Benoît

2018-01-01

Molecular dynamics (MD) trajectories based on classical equations of motion can be used to sample the configurational space of complex molecular systems. However, brute-force MD often converges slowly due to the ruggedness of the underlying potential energy surface. Several schemes have been proposed to address this problem by effectively smoothing the potential energy surface. However, in order to recover the proper Boltzmann equilibrium probability distribution, these approaches must then rely on statistical reweighting techniques or generate the simulations within a Hamiltonian tempering replica-exchange scheme. The present work puts forth a novel hybrid sampling propagator combining Metropolis-Hastings Monte Carlo (MC) with proposed moves generated by non-equilibrium MD (neMD). This hybrid neMD-MC propagator comprises three elementary elements: (i) an atomic system is dynamically propagated for some period of time using standard equilibrium MD on the correct potential energy surface; (ii) the system is then propagated for a brief period of time during what is referred to as a "boosting phase," via a time-dependent Hamiltonian that is evolved toward the perturbed potential energy surface and then back to the correct potential energy surface; (iii) the resulting configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolis criterion before returning to step 1. A symmetric two-end momentum reversal prescription is used at the end of the neMD trajectories to guarantee that the hybrid neMD-MC sampling propagator obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The hybrid neMD-MC sampling propagator is designed and implemented to enhance the sampling by relying on the accelerated MD and solute tempering schemes. It is also combined with the adaptive biased force sampling algorithm to examine. Illustrative tests with specific biomolecular systems indicate that the method can yield

Enhanced configurational sampling with hybrid non-equilibrium molecular dynamics-Monte Carlo propagator.

PubMed

Suh, Donghyuk; Radak, Brian K; Chipot, Christophe; Roux, Benoît

2018-01-07

Molecular dynamics (MD) trajectories based on classical equations of motion can be used to sample the configurational space of complex molecular systems. However, brute-force MD often converges slowly due to the ruggedness of the underlying potential energy surface. Several schemes have been proposed to address this problem by effectively smoothing the potential energy surface. However, in order to recover the proper Boltzmann equilibrium probability distribution, these approaches must then rely on statistical reweighting techniques or generate the simulations within a Hamiltonian tempering replica-exchange scheme. The present work puts forth a novel hybrid sampling propagator combining Metropolis-Hastings Monte Carlo (MC) with proposed moves generated by non-equilibrium MD (neMD). This hybrid neMD-MC propagator comprises three elementary elements: (i) an atomic system is dynamically propagated for some period of time using standard equilibrium MD on the correct potential energy surface; (ii) the system is then propagated for a brief period of time during what is referred to as a "boosting phase," via a time-dependent Hamiltonian that is evolved toward the perturbed potential energy surface and then back to the correct potential energy surface; (iii) the resulting configuration at the end of the neMD trajectory is then accepted or rejected according to a Metropolis criterion before returning to step 1. A symmetric two-end momentum reversal prescription is used at the end of the neMD trajectories to guarantee that the hybrid neMD-MC sampling propagator obeys microscopic detailed balance and rigorously yields the equilibrium Boltzmann distribution. The hybrid neMD-MC sampling propagator is designed and implemented to enhance the sampling by relying on the accelerated MD and solute tempering schemes. It is also combined with the adaptive biased force sampling algorithm to examine. Illustrative tests with specific biomolecular systems indicate that the method can yield

The lucky image-motion prediction for simple scene observation based soft-sensor technology

NASA Astrophysics Data System (ADS)

Li, Yan; Su, Yun; Hu, Bin

2015-08-01

High resolution is important to earth remote sensors, while the vibration of the platforms of the remote sensors is a major factor restricting high resolution imaging. The image-motion prediction and real-time compensation are key technologies to solve this problem. For the reason that the traditional autocorrelation image algorithm cannot meet the demand for the simple scene image stabilization, this paper proposes to utilize soft-sensor technology in image-motion prediction, and focus on the research of algorithm optimization in imaging image-motion prediction. Simulations results indicate that the improving lucky image-motion stabilization algorithm combining the Back Propagation Network (BP NN) and support vector machine (SVM) is the most suitable for the simple scene image stabilization. The relative error of the image-motion prediction based the soft-sensor technology is below 5%, the training computing speed of the mathematical predication model is as fast as the real-time image stabilization in aerial photography.

Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.

PubMed

Xie, Suchao; Zhou, Hui

2014-01-01

In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times

BP5 monolayer with multiferroicity and negative Poisson’s ratio: a prediction by global optimization method

NASA Astrophysics Data System (ADS)

Wang, Haidi; Li, Xingxing; Sun, Jiuyu; Liu, Zhao; Yang, Jinlong

2017-12-01

Based on global optimization Cuttlefish algorithm, we predict a stable two-dimensional (2D) phase of boron phosphide with 1:5 stoichiometry, i.e. boron pentaphosphide (BP5) monolayer, which has a lower formation energy than that of the commonly believed graphitic phase (g-BP). BP5 monolayer is a multiferroic material with coupled ferroelasticity and ferroelectricity. The predicted reversible strain is up to 41.41%, which is the largest one among all reported ferroelastic materials. Due to the non-centrosymmetric structure and electronegativity differences between boron and phosphorus atoms, an in-plane spontaneous polarization of 1.63  ×  10-10 C m-1 occurs in BP5. Moreover, the recently hunted negative Poisson’s ratio property, is also observed in BP5. As an indirect semiconductor with a band gap of 1.34 eV, BP5 displays outstanding optical and electronic properties, for instance strongly anisotropic visible-light absorption and high carrier mobility. Finally, we demonstrate that AlN (0 1 0) surface could be a suitable substrate for epitaxy growth of BP5 monolayer. Due to the rich and extraordinary properties of BP5, it’s considered to be a potential nanomaterial for designing electromechanical or optoelectronic devices, such as nonvolatile memory with conveniently readable/writeable capability.

Active action potential propagation but not initiation in thalamic interneuron dendrites

PubMed Central

Casale, Amanda E.; McCormick, David A.

2012-01-01

Inhibitory interneurons of the dorsal lateral geniculate nucleus of the thalamus modulate the activity of thalamocortical cells in response to excitatory input through the release of inhibitory neurotransmitter from both axons and dendrites. The exact mechanisms by which release can occur from dendrites are, however, not well understood. Recent experiments using calcium imaging have suggested that Na/K based action potentials can evoke calcium transients in dendrites via local active conductances, making the back-propagating action potential a candidate for dendritic neurotransmitter release. In this study, we employed high temporal and spatial resolution voltage-sensitive dye imaging to assess the characteristics of dendritic voltage deflections in response to Na/K action potentials in interneurons of the mouse dorsal lateral geniculate nucleus. We found that trains or single action potentials elicited by somatic current injection or local synaptic stimulation led to action potentials that rapidly and actively back-propagated throughout the entire dendritic arbor and into the fine filiform dendritic appendages known to release GABAergic vesicles. Action potentials always appeared first in the soma or proximal dendrite in response to somatic current injection or local synaptic stimulation, and the rapid back-propagation into the dendritic arbor depended upon voltage-gated sodium and TEA-sensitive potassium channels. Our results indicate that thalamic interneuron dendrites integrate synaptic inputs that initiate action potentials, most likely in the axon initial segment, that then back-propagate with high-fidelity into the dendrites, resulting in a nearly synchronous release of GABA from both axonal and dendritic compartments. PMID:22171033

SU-F-SPS-06: Implementation of a Back-Projection Algorithm for 2D in Vivo Dosimetry with An EPID System

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

Hernandez Reyes, B; Rodriguez Perez, E; Sosa Aquino, M

Purpose: To implement a back-projection algorithm for 2D dose reconstructions for in vivo dosimetry in radiation therapy using an Electronic Portal Imaging Device (EPID) based on amorphous silicon. Methods: An EPID system was used to calculate dose-response function, pixel sensitivity map, exponential scatter kernels and beam hardenig correction for the back-projection algorithm. All measurements were done with a 6 MV beam. A 2D dose reconstruction for an irradiated water phantom (30×30×30 cm{sup 3}) was done to verify the algorithm implementation. Gamma index evaluation between the 2D reconstructed dose and the calculated with a treatment planning system (TPS) was done. Results:more » A linear fit was found for the dose-response function. The pixel sensitivity map has a radial symmetry and was calculated with a profile of the pixel sensitivity variation. The parameters for the scatter kernels were determined only for a 6 MV beam. The primary dose was estimated applying the scatter kernel within EPID and scatter kernel within the patient. The beam hardening coefficient is σBH= 3.788×10{sup −4} cm{sup 2} and the effective linear attenuation coefficient is µAC= 0.06084 cm{sup −1}. The 95% of points evaluated had γ values not longer than the unity, with gamma criteria of ΔD = 3% and Δd = 3 mm, and within the 50% isodose surface. Conclusion: The use of EPID systems proved to be a fast tool for in vivo dosimetry, but the implementation is more complex that the elaborated for pre-treatment dose verification, therefore, a simplest method must be investigated. The accuracy of this method should be improved modifying the algorithm in order to compare lower isodose curves.« less

Fractional-order gradient descent learning of BP neural networks with Caputo derivative.

PubMed

Wang, Jian; Wen, Yanqing; Gou, Yida; Ye, Zhenyun; Chen, Hua

2017-05-01

Fractional calculus has been found to be a promising area of research for information processing and modeling of some physical systems. In this paper, we propose a fractional gradient descent method for the backpropagation (BP) training of neural networks. In particular, the Caputo derivative is employed to evaluate the fractional-order gradient of the error defined as the traditional quadratic energy function. The monotonicity and weak (strong) convergence of the proposed approach are proved in detail. Two simulations have been implemented to illustrate the performance of presented fractional-order BP algorithm on three small datasets and one large dataset. The numerical simulations effectively verify the theoretical observations of this paper as well. Copyright © 2017 Elsevier Ltd. All rights reserved.

In vitro propagation of Paphiopedilum orchids.

PubMed

Zeng, Songjun; Huang, Weichang; Wu, Kunlin; Zhang, Jianxia; da Silva, Jaime A Teixeira; Duan, Jun

2016-01-01

Paphiopedilum is one of the most popular and rare orchid genera. Members of the genus are sold and exhibited as pot plants and cut flowers. Wild populations of Paphiopedilum are under the threat of extinction due to over-collection and loss of suitable habitats. A reduction in their commercial value through large-scale propagation in vitro is an option to reduce pressure from illegal collection, to attempt to meet commercial needs and to re-establish threatened species back into the wild. Although they are commercially propagated via asymbiotic seed germination, Paphiopedilum are considered to be difficult to propagate in vitro, especially by plant regeneration from tissue culture. This review aims to cover the most important aspects and to provide an up-to-date research progress on in vitro propagation of Paphiopedilum and to emphasize the importance of further improving tissue culture protocols for ex vitro-derived explants.

Enhancing data locality by using terminal propagation

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

Hendrickson, B.; Leland, R.; Van Driessche, R.

1995-12-31

Terminal propagation is a method developed in the circuit placement community for adding constraints to graph partitioning problems. This paper adapts and expands this idea, and applies it to the problem of partitioning data structures among the processors of a parallel computer. We show how the constraints in terminal propagation can be used to encourage partitions in which messages are communicated only between architecturally near processors. We then show how these constraints can be handled in two important partitioning algorithms, spectral bisection and multilevel-KL. We compare the quality of partitions generated by these algorithms to each other and to Partitionsmore » generated by more familiar techniques.« less

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

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

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

Consistency functional map propagation for repetitive patterns

NASA Astrophysics Data System (ADS)

Wang, Hao

2017-09-01

Repetitive patterns appear frequently in both man-made and natural environments. Automatically and robustly detecting such patterns from an image is a challenging problem. We study repetitive pattern alignment by embedding segmentation cue with a functional map model. However, this model cannot tackle the repetitive patterns directly due to the large photometric and geometric variations. Thus, a consistency functional map propagation (CFMP) algorithm that extends the functional map with dynamic propagation is proposed to address this issue. This propagation model is acquired in two steps. The first one aligns the patterns from a local region, transferring segmentation functions among patterns. It can be cast as an L norm optimization problem. The latter step updates the template segmentation for the next round of pattern discovery by merging the transferred segmentation functions. Extensive experiments and comparative analyses have demonstrated an encouraging performance of the proposed algorithm in detection and segmentation of repetitive patterns.

Analysis of pulse thermography using similarities between wave and diffusion propagation

NASA Astrophysics Data System (ADS)

Gershenson, M.

2017-05-01

Pulse thermography or thermal wave imaging are commonly used as nondestructive evaluation (NDE) method. While the technical aspect has evolve with time, theoretical interpretation is lagging. Interpretation is still using curved fitting on a log log scale. A new approach based directly on the governing differential equation is introduced. By using relationships between wave propagation and the diffusive propagation of thermal excitation, it is shown that one can transform from solutions in one type of propagation to the other. The method is based on the similarities between the Laplace transforms of the diffusion equation and the wave equation. For diffusive propagation we have the Laplace variable s to the first power, while for the wave propagation similar equations occur with s2. For discrete time the transformation between the domains is performed by multiplying the temperature data vector by a matrix. The transform is local. The performance of the techniques is tested on synthetic data. The application of common back projection techniques used in the processing of wave data is also demonstrated. The combined use of the transform and back projection makes it possible to improve both depth and lateral resolution of transient thermography.

Numerical simulations of detonation propagation in gaseous fuel-air mixtures

NASA Astrophysics Data System (ADS)

Honhar, Praveen; Kaplan, Carolyn; Houim, Ryan; Oran, Elaine

2017-11-01

Unsteady multidimensional numerical simulations of detonation propagation and survival in mixtures of fuel (hydrogen or methane) diluted with air were carried out with a fully compressible Navier-Stokes solver using a simplified chemical-diffusive model (CDM). The CDM was derived using a genetic algorithm combined with the Nelder-Mead optimization algorithm and reproduces physically correct laminar flame and detonation properties. Cases studied are overdriven detonations propagating through confined mediums, with or without gradients in composition. Results from simulations confirm that the survival of the detonation depends on the channel heights. In addition, the simulations show that the propagation of the detonation waves depends on the steepness in composition gradients.

Polymer-Based Black Phosphorus (bP) Hybrid Materials by in Situ Radical Polymerization: An Effective Tool To Exfoliate bP and Stabilize bP Nanoflakes

PubMed Central

2018-01-01

Black phosphorus (bP) has been recently investigated for next generation nanoelectronic multifunctional devices. However, the intrinsic instability of exfoliated bP (the bP nanoflakes) toward both moisture and air has so far overshadowed its practical implementation. In order to contribute to fill this gap, we report here the preparation of new hybrid polymer-based materials where bP nanoflakes (bPn) exhibit a significantly improved stability. The new materials have been prepared by different synthetic paths including: (i) the mixing of conventionally liquid-phase exfoliated bP (in dimethyl sulfoxide, DMSO) with poly(methyl methacrylate) (PMMA) solution; (ii) the direct exfoliation of bP in a polymeric solution; (iii) the in situ radical polymerization after exfoliating bP in the liquid monomer (methyl methacrylate, MMA). This last methodology concerns the preparation of stable suspensions of bPn–MMA by sonication-assisted liquid-phase exfoliation (LPE) of bP in the presence of MMA followed by radical polymerization. The hybrids characteristics have been compared in order to evaluate the bP dispersion and the effectiveness of the bPn interfacial interactions with polymer chains aimed at their long-term environmental stabilization. The passivation of the bPn is particularly effective when the hybrid material is prepared by in situ polymerization. By using this synthetic methodology, the nanoflakes, even if with a gradient of dispersion (size of aggregates), preserve their chemical structure from oxidation (as proved by both Raman and 31P-solid state NMR studies) and are particularly stable to air and UV light exposure. The feasibility of this approach, capable of efficiently exfoliating bP while protecting the bPn, has been then verified by using different vinyl monomers (styrene and N-vinylpyrrolidone), thus obtaining hybrids where the nanoflakes are embedded in polymer matrices with a variety of intriguing thermal, mechanical, and solubility characteristics.

Visual attitude propagation for small satellites

NASA Astrophysics Data System (ADS)

Rawashdeh, Samir A.

As electronics become smaller and more capable, it has become possible to conduct meaningful and sophisticated satellite missions in a small form factor. However, the capability of small satellites and the range of possible applications are limited by the capabilities of several technologies, including attitude determination and control systems. This dissertation evaluates the use of image-based visual attitude propagation as a compliment or alternative to other attitude determination technologies that are suitable for miniature satellites. The concept lies in using miniature cameras to track image features across frames and extracting the underlying rotation. The problem of visual attitude propagation as a small satellite attitude determination system is addressed from several aspects: related work, algorithm design, hardware and performance evaluation, possible applications, and on-orbit experimentation. These areas of consideration reflect the organization of this dissertation. A "stellar gyroscope" is developed, which is a visual star-based attitude propagator that uses relative motion of stars in an imager's field of view to infer the attitude changes. The device generates spacecraft relative attitude estimates in three degrees of freedom. Algorithms to perform the star detection, correspondence, and attitude propagation are presented. The Random Sample Consensus (RANSAC) approach is applied to the correspondence problem to successfully pair stars across frames while mitigating falsepositive and false-negative star detections. This approach provides tolerance to the noise levels expected in using miniature optics and no baffling, and the noise caused by radiation dose on orbit. The hardware design and algorithms are validated using test images of the night sky. The application of the stellar gyroscope as part of a CubeSat attitude determination and control system is described. The stellar gyroscope is used to augment a MEMS gyroscope attitude propagation

Semiclassical propagation of Wigner functions.

PubMed

Dittrich, T; Gómez, E A; Pachón, L A

2010-06-07

We present a comprehensive study of semiclassical phase-space propagation in the Wigner representation, emphasizing numerical applications, in particular as an initial-value representation. Two semiclassical approximation schemes are discussed. The propagator of the Wigner function based on van Vleck's approximation replaces the Liouville propagator by a quantum spot with an oscillatory pattern reflecting the interference between pairs of classical trajectories. Employing phase-space path integration instead, caustics in the quantum spot are resolved in terms of Airy functions. We apply both to two benchmark models of nonlinear molecular potentials, the Morse oscillator and the quartic double well, to test them in standard tasks such as computing autocorrelation functions and propagating coherent states. The performance of semiclassical Wigner propagation is very good even in the presence of marked quantum effects, e.g., in coherent tunneling and in propagating Schrodinger cat states, and of classical chaos in four-dimensional phase space. We suggest options for an effective numerical implementation of our method and for integrating it in Monte-Carlo-Metropolis algorithms suitable for high-dimensional systems.

A wide-angle high Mach number modal expansion for infrasound propagation.

PubMed

Assink, Jelle; Waxler, Roger; Velea, Doru

2017-03-01

The use of modal expansions to solve the problem of atmospheric infrasound propagation is revisited. A different form of the associated modal equation is introduced, valid for wide-angle propagation in atmospheres with high Mach number flow. The modal equation can be formulated as a quadratic eigenvalue problem for which there are simple and efficient numerical implementations. A perturbation expansion for the treatment of attenuation, valid for stratified media with background flow, is derived as well. Comparisons are carried out between the proposed algorithm and a modal algorithm assuming an effective sound speed, including a real data case study. The comparisons show that the effective sound speed approximation overestimates the effect of horizontal wind on sound propagation, leading to errors in traveltime, propagation path, trace velocity, and absorption. The error is found to be dependent on propagation angle and Mach number.

A back-projection algorithm in the presence of an extra attenuating medium: towards EPID dosimetry for the MR-Linac

NASA Astrophysics Data System (ADS)

Torres-Xirau, I.; Olaciregui-Ruiz, I.; Rozendaal, R. A.; González, P.; Mijnheer, B. J.; Sonke, J.-J.; van der Heide, U. A.; Mans, A.

2017-08-01

In external beam radiotherapy, electronic portal imaging devices (EPIDs) are frequently used for pre-treatment and for in vivo dose verification. Currently, various MR-guided radiotherapy systems are being developed and clinically implemented. Independent dosimetric verification is highly desirable. For this purpose we adapted our EPID-based dose verification system for use with the MR-Linac combination developed by Elekta in cooperation with UMC Utrecht and Philips. In this study we extended our back-projection method to cope with the presence of an extra attenuating medium between the patient and the EPID. Experiments were performed at a conventional linac, using an aluminum mock-up of the MRI scanner housing between the phantom and the EPID. For a 10 cm square field, the attenuation by the mock-up was 72%, while 16% of the remaining EPID signal resulted from scattered radiation. 58 IMRT fields were delivered to a 20 cm slab phantom with and without the mock-up. EPID reconstructed dose distributions were compared to planned dose distributions using the γ -evaluation method (global, 3%, 3 mm). In our adapted back-projection algorithm the averaged {γmean} was 0.27+/- 0.06 , while in the conventional it was 0.28+/- 0.06 . Dose profiles of several square fields reconstructed with our adapted algorithm showed excellent agreement when compared to TPS.

Role of AMPA and NMDA receptors and back-propagating action potentials in spike timing-dependent plasticity.

PubMed

Fuenzalida, Marco; Fernández de Sevilla, David; Couve, Alejandro; Buño, Washington

2010-01-01

The cellular mechanisms that mediate spike timing-dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSP(AMPA) and EPSP(NMDA)) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSP(AMPA) with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSP(AMPA) by combining SC stimulation, an imposed EPSP(AMPA)-like depolarization, and BAP or by coupling the EPSP(NMDA) evoked under sustained depolarization (approximately -40 mV) and BAP. In Mg(2+)-free solution EPSP(NMDA) and BAP also produced LTP. Suppression of EPSP(NMDA) or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.

Variational and symplectic integrators for satellite relative orbit propagation including drag

NASA Astrophysics Data System (ADS)

Palacios, Leonel; Gurfil, Pini

2018-04-01

Orbit propagation algorithms for satellite relative motion relying on Runge-Kutta integrators are non-symplectic—a situation that leads to incorrect global behavior and degraded accuracy. Thus, attempts have been made to apply symplectic methods to integrate satellite relative motion. However, so far all these symplectic propagation schemes have not taken into account the effect of atmospheric drag. In this paper, drag-generalized symplectic and variational algorithms for satellite relative orbit propagation are developed in different reference frames, and numerical simulations with and without the effect of atmospheric drag are presented. It is also shown that high-order versions of the newly-developed variational and symplectic propagators are more accurate and are significantly faster than Runge-Kutta-based integrators, even in the presence of atmospheric drag.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

An algorithmic programming approach for back pain symptoms in failed back surgery syndrome using spinal cord stimulation with a multicolumn surgically implanted epidural lead: a multicenter international prospective study.

PubMed

Rigoard, Philippe; Jacques, Line; Delmotte, Alexandre; Poon, Katherine; Munson, Russell; Monlezun, Olivier; Roulaud, Manuel; Prevost, Audrey; Guetarni, Farid; Bataille, Benoit; Kumar, Krishna

2015-03-01

Many studies have demonstrated the efficacy and the medical/economic value of epidural spinal cord stimulation for the treatment of "failed back surgery syndrome" (FBSS). However, the back pain component of FBSS has been recalcitrant. Recent clinical trials have suggested that multicolumn surgically implanted leads combined with enhanced programming capabilities in the newer implantable pulse generators demonstrate the ability to treat the back pain component of FBSS. The objective of our present international multicentre study is to prospectively evaluate these findings in a larger population. We conducted a prospective, nonrandomized, observational study on 76 patients with refractory FBSS, consecutively implanted with multicolumn spinal cord stimulation (SCS) between 2008 and 2011 in three neurosurgical pain management centers (Poitiers, France; Montréal, Canada; and Regina, Canada). The primary objective of this study was to prospectively analyze the effect of multicolumn lead programming on paresthesia coverage for the back pain region in these patients. The secondary objective was to assess the analgesic efficacy of this technique on the global and back pain components. Paresthesia could be induced in the lower extremities in the majority of patients with at least one of the configurations tested. Bilateral low back paresthesia was induced in 53.5% of patients, while unilateral low back paresthesia was induced in 78.9% of patients. Multicolumn configurations were statistically more effective than monocolumn configurations for all anatomic regions studied. At 6 months, 75.4% of patients receiving multicolumn stimulation (n = 57) obtained at least a 30% improvement of the back pain VAS score, while 42.1% of patients obtained at least a 50% improvement of the back pain VAS score. This study confirms the hypothesis that multicolumn SCS should be considered as an important tool in the treatment of radicular and axial pain in FBSS patients. The efficacy of this

Uniformly stable backpropagation algorithm to train a feedforward neural network.

PubMed

Rubio, José de Jesús; Angelov, Plamen; Pacheco, Jaime

2011-03-01

Neural networks (NNs) have numerous applications to online processes, but the problem of stability is rarely discussed. This is an extremely important issue because, if the stability of a solution is not guaranteed, the equipment that is being used can be damaged, which can also cause serious accidents. It is true that in some research papers this problem has been considered, but this concerns continuous-time NN only. At the same time, there are many systems that are better described in the discrete time domain such as population of animals, the annual expenses in an industry, the interest earned by a bank, or the prediction of the distribution of loads stored every hour in a warehouse. Therefore, it is of paramount importance to consider the stability of the discrete-time NN. This paper makes several important contributions. 1) A theorem is stated and proven which guarantees uniform stability of a general discrete-time system. 2) It is proven that the backpropagation (BP) algorithm with a new time-varying rate is uniformly stable for online identification and the identification error converges to a small zone bounded by the uncertainty. 3) It is proven that the weights' error is bounded by the initial weights' error, i.e., overfitting is eliminated in the proposed algorithm. 4) The BP algorithm is applied to predict the distribution of loads that a transelevator receives from a trailer and places in the deposits in a warehouse every hour, so that the deposits in the warehouse are reserved in advance using the prediction results. 5) The BP algorithm is compared with the recursive least square (RLS) algorithm and with the Takagi-Sugeno type fuzzy inference system in the problem of predicting the distribution of loads in a warehouse, giving that the first and the second are stable and the third is unstable. 6) The BP algorithm is compared with the RLS algorithm and with the Kalman filter algorithm in a synthetic example.

An automated workflow for patient-specific quality control of contour propagation

NASA Astrophysics Data System (ADS)

Beasley, William J.; McWilliam, Alan; Slevin, Nicholas J.; Mackay, Ranald I.; van Herk, Marcel

2016-12-01

Contour propagation is an essential component of adaptive radiotherapy, but current contour propagation algorithms are not yet sufficiently accurate to be used without manual supervision. Manual review of propagated contours is time-consuming, making routine implementation of real-time adaptive radiotherapy unrealistic. Automated methods of monitoring the performance of contour propagation algorithms are therefore required. We have developed an automated workflow for patient-specific quality control of contour propagation and validated it on a cohort of head and neck patients, on which parotids were outlined by two observers. Two types of error were simulated—mislabelling of contours and introducing noise in the scans before propagation. The ability of the workflow to correctly predict the occurrence of errors was tested, taking both sets of observer contours as ground truth, using receiver operator characteristic analysis. The area under the curve was 0.90 and 0.85 for the observers, indicating good ability to predict the occurrence of errors. This tool could potentially be used to identify propagated contours that are likely to be incorrect, acting as a flag for manual review of these contours. This would make contour propagation more efficient, facilitating the routine implementation of adaptive radiotherapy.

BP network for atorvastatin effect evaluation from ultrasound images features classification

NASA Astrophysics Data System (ADS)

Fang, Mengjie; Yang, Xin; Liu, Yang; Xu, Hongwei; Liang, Huageng; Wang, Yujie; Ding, Mingyue

2013-10-01

Atherosclerotic lesions at the carotid artery are a major cause of emboli or atheromatous debris, resulting in approximately 88% of ischemic strokes in the USA in 2006. Stroke is becoming the most common cause of death worldwide, although patient management and prevention strategies have reduced stroke rate considerably over the past decades. Many research studies have been carried out on how to quantitatively evaluate local arterial effects for potential carotid disease treatments. As an inexpensive, convenient and fast means of detection, ultrasonic medical testing has been widespread in the world, so it is very practical to use ultrasound technology in the prevention and treatment of carotid atherosclerosis. This paper is dedicated to this field. Currently, many ultrasound image characteristics on carotid plaque have been proposed. After screening a large number of features (including 26 morphological and 85 texture features), we have got six shape characteristics and six texture characteristics in the combination. In order to test the validity and accuracy of these combined features, we have established a Back-Propagation (BP) neural network to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for the evaluation. The classification results showed that the combined features and classification have good recognition ability, with the overall accuracy 83.93%, sensitivity 82.14%, specificity 85.20%, positive predictive value 79.86%, negative predictive value 86.98%, Matthew's correlation coefficient 67.08%, and Youden's index 67.34%. And the receiver operating characteristic (ROC) curve in our test also performed well.

Uncertainties of optical parameters and their propagations in an analytical ocean color inversion algorithm.

PubMed

Lee, ZhongPing; Arnone, Robert; Hu, Chuanmin; Werdell, P Jeremy; Lubac, Bertrand

2010-01-20

Following the theory of error propagation, we developed analytical functions to illustrate and evaluate the uncertainties of inherent optical properties (IOPs) derived by the quasi-analytical algorithm (QAA). In particular, we evaluated the effects of uncertainties of these optical parameters on the inverted IOPs: the absorption coefficient at the reference wavelength, the extrapolation of particle backscattering coefficient, and the spectral ratios of absorption coefficients of phytoplankton and detritus/gelbstoff, respectively. With a systematically simulated data set (46,200 points), we found that the relative uncertainty of QAA-derived total absorption coefficients in the blue-green wavelengths is generally within +/-10% for oceanic waters. The results of this study not only establish theoretical bases to evaluate and understand the effects of the various variables on IOPs derived from remote-sensing reflectance, but also lay the groundwork to analytically estimate uncertainties of these IOPs for each pixel. These are required and important steps for the generation of quality maps of IOP products derived from satellite ocean color remote sensing.

Rupture Processes of the Mw8.3 Sea of Okhotsk Earthquake and Aftershock Sequences from 3-D Back Projection Imaging

NASA Astrophysics Data System (ADS)

Jian, P. R.; Hung, S. H.; Meng, L.

2014-12-01

On May 24, 2013, the largest deep earthquake ever recorded in history occurred on the southern tip of the Kamchatka Island, where the Pacific Plate subducts underneath the Okhotsk Plate. Previous 2D beamforming back projection (BP) of P- coda waves suggests the mainshock ruptured bilaterally along a horizontal fault plane determined by the global centroid moment tensor solution. On the other hand, the multiple point source inversion of P and SH waveforms argued that the earthquake comprises a sequence of 6 subevents not located on a single plane but actually distributed in a zone that extends 64 km horizontally and 35 km in depth. We then apply a three-dimensional MUSIC BP approach to resolve the rupture processes of the manishock and two large aftershocks (M6.7) with no a priori setup of preferential orientations of the planar rupture. The maximum pseudo-spectrum of high-frequency P wave in a sequence of time windows recorded by the densely-distributed stations from US and EU Array are used to image 3-D temporal and spatial rupture distribution. The resulting image confirms that the nearly N-S striking but two antiparallel rupture stages. The first subhorizontal rupture initially propagates toward the NNE direction, while at 18 s later it directs reversely to the SSW and concurrently shifts downward to 35 km deeper lasting for about 20 s. The rupture lengths in the first NNE-ward and second SSW-ward stage are about 30 km and 85 km; the estimated rupture velocities are 3 km/s and 4.25 km/s, respectively. Synthetic experiments are undertaken to assess the capability of the 3D MUSIC BP for the recovery of spatio-temporal rupture processes. Besides, high frequency BP images based on the EU-Array data show two M6.7 aftershocks are more likely to rupture on the vertical fault planes.

Wave propagation, scattering and emission in complex media

NASA Astrophysics Data System (ADS)

Jin, Ya-Qiu

. Gitterman. Transformation of the spectrum of scattered radiation in randomly inhomogeneous absorptive plasma layer / G. V. Jandieri, G. D. Aburjunia, V. G. Jandieri. Numerical analysis of microwave heating on saponification reaction / K. Huang, K. Jia -- IV. Scattering from complex targets. Analysis of electromagnetic scattering from layered crossed-gratings of circular cylinders using lattice sums technique / K. Yasumoto, H. T. Jia. Scattering by a body in a random medium / M. Tateiba, Z. Q. Meng, H. El-Ocla. A rigorous analysis of electromagnetic scattering from multilayered crossed-arrays of metallic cylinders / H. T. Jia, K. Yasumoto. Vector models of non-stable and spatially-distributed radar objects / A. Surkov ... [et al.]. Simulation of algorithm of orthogonal signals forming and processing used to estimate back scattering matrix of non-stable radar objects / D. Nosov ... [et al.]. New features of scattering from a dielectric film on a reflecting metal substrate / Z. H. Gu, I. M. Fuks, M. Ciftan. A higher order FDTD method for EM wave propagation in collision plasmas / S. B. Liu, J. J. Mo, N. C. Yuan -- V. Radiative transfer and remote sensing. Simulating microwave emission from Antarctica ice sheet with a coherent model / M. Tedesco, P. Pampaloni. Scattering and emission from inhomogeneous vegetation canopy and alien target by using three-dimensional Vector Radiative Transfer (3D-VRT) equation / Y. Q. Jin, Z. C. Liang. Analysis of land types using high-resolution satellite images and fractal approach / H. G. Zhang ... [et al.]. Data fusion of RADARSAT SAR and DMSP SSM/I for monitoring sea ice of China's Bohai Sea / Y. Q. Jin. Retrieving atmospheric temperature profiles from simulated microwave radiometer data with artificial neural networks / Z. G. Yao, H. B. Chen -- VI. Wave propagation and wireless communication. Wireless propagation in urban environments: modeling and experimental verification / D. Erricolo ... [et al.]. An overview of physics-based wave

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

NASA Astrophysics Data System (ADS)

Yip, Stephen S. F.; Coroller, Thibaud P.; Sanford, Nina N.; Huynh, Elizabeth; Mamon, Harvey; Aerts, Hugo J. W. L.; Berbeco, Ross I.

2016-01-01

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI  =  58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI  =  69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC  =  0.58, q-value  =  0.33), while the differentiation was significant with other textures (AUC  =  0.71‒0.73, p  <  0.009). Among the deformable algorithms, fast-demons (AUC  =  0.68‒0.70, q-value  <  0.03) and fast-free-form (AUC  =  0.69‒0.74, q-value  <  0.04) were the least predictive. ROIs propagated by all other

Liouvillian propagators, Riccati equation and differential Galois theory

NASA Astrophysics Data System (ADS)

Acosta-Humánez, Primitivo; Suazo, Erwin

2013-11-01

In this paper a Galoisian approach to building propagators through Riccati equations is presented. The main result corresponds to the relationship between the Galois integrability of the linear Schrödinger equation and the virtual solvability of the differential Galois group of its associated characteristic equation. As the main application of this approach we solve Ince’s differential equation through the Hamiltonian algebrization procedure and the Kovacic algorithm to find the propagator for a generalized harmonic oscillator. This propagator has applications which describe the process of degenerate parametric amplification in quantum optics and light propagation in a nonlinear anisotropic waveguide. Toy models of propagators inspired by integrable Riccati equations and integrable characteristic equations are also presented.

The prediction of the building precision in the Laser Engineered Net Shaping process using advanced networks

NASA Astrophysics Data System (ADS)

Lu, Z. L.; Li, D. C.; Lu, B. H.; Zhang, A. F.; Zhu, G. X.; Pi, G.

2010-05-01

Laser Engineered Net Shaping (LENS) is an advanced manufacturing technology, but it is difficult to control the depositing height (DH) of the prototype because there are many technology parameters influencing the forming process. The effect of main parameters (laser power, scanning speed and powder feeding rate) on the DH of single track is firstly analyzed, and then it shows that there is the complex nonlinear intrinsic relationship between them. In order to predict the DH, the back propagation (BP) based network improved with Adaptive learning rate and Momentum coefficient (AM) algorithm, and the least square support vector machine (LS-SVM) network are both adopted. The mapping relationship between above parameters and the DH is constructed according to training samples collected by LENS experiments, and then their generalization ability, function-approximating ability and real-time are contrastively investigated. The results show that although the predicted result by the BP-AM approximates the experimental result, above performance index of the LS-SVM are better than those of the BP-AM. Finally, high-definition thin-walled parts of AISI316L are successfully fabricated. Hence, the LS-SVM network is more suitable for the prediction of the DH.

One-year results of an algorithmic approach to managing failed back surgery syndrome

PubMed Central

Avellanal, Martín; Diaz-Reganon, Gonzalo; Orts, Alejandro; Soto, Silvia

2014-01-01

BACKGROUND: Failed back surgery syndrome (FBSS) is a major clinical problem. Different etiologies with different incidence rates have been proposed. There are currently no standards regarding the management of these patients. Epiduroscopy is an endoscopic technique that may play a role in the management of FBSS. OBJECTIVE: To evaluate an algorithm for management of severe FBSS including epiduroscopy as a diagnostic and therapeutic tool. METHODS: A total of 133 patients with severe symptoms of FBSS (visual analogue scale score ≥7) and no response to pharmacological treatment and physical therapy were included. A six-step management algorithm was applied. Data, including patient demographics, pain and surgical procedure, were analyzed. In all cases, one or more objective causes of pain were established. Treatment success was defined as ≥50% long-term pain relief maintained during the first year of follow-up. Final allocation of patients was registered: good outcome with conservative treatment, surgical reintervention and palliative treatment with implantable devices. RESULTS: Of 122 patients enrolled, 59.84% underwent instrumented surgery and 40.16% a noninstrumented procedure. Most (64.75%) experienced significant pain relief with conventional pain clinic treatments; 15.57% required surgical treatment. Palliative spinal cord stimulation and spinal analgesia were applied in 9.84% and 2.46% of the cases, respectively. The most common diagnosis was epidural fibrosis, followed by disc herniation, global or lateral stenosis, and foraminal stenosis. CONCLUSIONS: A new six-step ladder approach to severe FBSS management that includes epiduroscopy was analyzed. Etiologies are accurately described and a useful role of epiduroscopy was confirmed. PMID:25222573

Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

NASA Astrophysics Data System (ADS)

Hazza, Muataz Hazza F. Al; Adesta, Erry Y. T.; Riza, Muhammad

2013-12-01

High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models.

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

PubMed Central

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-01-01

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects. PMID:28773606

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation.

PubMed

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-06-17

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc. , it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

A portable back massage robot based on Traditional Chinese Medicine.

PubMed

Wang, Wendong; Liang, Chaohong; Zhang, Peng; Shi, Yikai

2018-05-30

A portable back massage robot which can complete the massage operations such as tapping, kneading and rolling was designed to improve the level of intelligence and massage effect. An efficient full covered path planning algorithm was put forward for a portable back massage robot to improve the coverage. Currently, massage robots has become one of important research focuses with the increasing requirements for healthcare. The massage robot is difficult to be widely accepted as there are problems of massage robot in control, structure, and coverage path planning. The 3D electromagnetic simulation model was established to optimize electromagnetic force. By analyzing the Traditional Chinese Medicine massage operation and the demands, the path planning algorithm models were established. The experimental platform of the massage robot was built. The simulation results show presented path planning algorithm is suitable for back massage, which ensures that the massage robot traverse the entire back area with improved massage coverage. The tested results show that the massage effect is best when the duty cycle is in the range of 1/8 to 1/2, and the massage force increases with the increase of the input voltage. The massage robot eventually achieved the desired massage effect, and the proposed efficient algorithm can effectively improve the coverage and promote the massage effect.

Usefulness of Enzyme-linked Immunosorbent Assay Using Recombinant BP180 and BP230 for Serodiagnosis and Monitoring Disease Activity of Bullous Pemphigoid

PubMed Central

Lee, Eui Hyung; Kim, Yeon Hee; Kim, Sinyoung; Kim, Song-ee

2012-01-01

Background Bullous pemphigoid (BP) is an autoimmune subepidermal bullous disease associated with autoantibodies against BP180 and BP230. Enzyme-linked immunosorbent assay (ELISA) is a sensitive tool for the detection of immunoglobulin G (IgG) anti-BP180 and anti-BP230 autoantibodies. Objective The aim of this study was to evaluate the usefulness of ELISA for diagnosing and monitoring the disease activity of BP. Methods We evaluated serum IgG levels of anti-BP180 and anti-BP230 autoantibodies in 47 BP patients, 16 epidermolysis bullosa aquisita patients, and 15 healthy volunteers using ELISA. Through retrospective review of the medical records, the clinical characteristics of BP including disease activity, duration, pruritus severity and peripheral blood eosinophil counts were assessed. Results The sensitivity of BP180 ELISA was 97.9%, BP230 ELISA 72.3%, and a combination of the two was 100%. The specificity of BP180 ELISA was 90.3%, BP230 ELISA 100%, and a combination of the two was 90.3%. BP180 ELISA scores showed strong associations with disease activity, pruritus severity, peripheral blood eosinophil counts, and disease duration, whereas BP230 ELISA scores did not. Conclusion BP180 and BP230 ELISAs are highly sensitive methods for the diagnosis of BP, and BP180 ELISA, in particular, is a sensitive tool for monitoring the disease activity of BP. PMID:22363155

Back-Projection Cortical Potential Imaging: Theory and Results.

PubMed

Haor, Dror; Shavit, Reuven; Shapiro, Moshe; Geva, Amir B

2017-07-01

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better

Three-dimensional propagation in near-field tomographic X-ray phase retrieval

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

Ruhlandt, Aike, E-mail: aruhlan@gwdg.de; Salditt, Tim

An extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions is presented, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. This paper presents an extension of phase retrieval algorithms for near-field X-ray (propagation) imaging to three dimensions, enhancing the quality of the reconstruction by exploiting previously unused three-dimensional consistency constraints. The approach is based on a novel three-dimensional propagator and is derived for the case of optically weak objects. It can be easily implemented in current phase retrieval architectures, is computationally efficient and reduces the need for restrictive prior assumptions, resultingmore » in superior reconstruction quality.« less

Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

NASA Astrophysics Data System (ADS)

Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

1995-02-01

We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

Filtered back-projection algorithm for Compton telescopes

DOEpatents

Gunter, Donald L [Lisle, IL

2008-03-18

A method for the conversion of Compton camera data into a 2D image of the incident-radiation flux on the celestial sphere includes detecting coincident gamma radiation flux arriving from various directions of a 2-sphere. These events are mapped by back-projection onto the 2-sphere to produce a convolution integral that is subsequently stereographically projected onto a 2-plane to produce a second convolution integral which is deconvolved by the Fourier method to produce an image that is then projected onto the 2-sphere.

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

Estimating EQ-5D values from the Oswestry Disability Index and numeric rating scales for back and leg pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-04-15

Cross-sectional cohort. The purpose of this study is to determine whether the EuroQOL-5D (EQ-5D) can be derived from commonly available low back disease-specific health-related quality of life measures. The Oswestry Disability Index (ODI) and numeric rating scales (0-10) for back pain (BP) and leg pain (LP) are widely used disease-specific measures in patients with lumbar degenerative disorders. Increasingly, the EQ-5D is being used as a measure of utility due to ease of administration and scoring. The EQ-5D, ODI, BP, and LP were prospectively collected in 14,544 patients seen in clinic for lumbar degenerative disorders. Pearson correlation coefficients for paired observations from multiple time points between ODI, BP, LP, and EQ-5D were determined. Regression modeling was done to compute the EQ-5D score from the ODI, BP, and LP. The mean age was 53.3 ± 16.4 years and 41% were male. Correlations between the EQ-5D and the ODI, BP, and LP were statistically significant (P < 0.0001) with correlation coefficients of -0.77, -0.50, and -0.57, respectively. The regression equation: [0.97711 + (-0.00687 × ODI) + (-0.01488 × LP) + (-0.01008 × BP)] to predict EQ-5D, had an R2 of 0.61 and a root mean square error of 0.149. The model using ODI alone had an R2 of 0.57 and a root mean square error of 0.156. The model using the individual ODI items had an R2 of 0.64 and a root mean square error of 0.143. The correlation coefficient between the observed and estimated EQ-5D score was 0.78. There was no statistically significant difference between the actual EQ-5D (0.553 ± 0.238) and the estimated EQ-5D score (0.553 ± 0.186) using the ODI, BP, and LP regression model. However, rounding off the coefficients to less than 5 decimal places produced less accurate results. Unlike previous studies showing a robust relationship between low back-specific measures and the Short Form-6D, a similar relationship was not seen between the ODI, BP, LP, and the EQ-5D. Thus, the EQ-5D cannot be

BP pledges to cut emissions

NASA Astrophysics Data System (ADS)

Showstack, Randy

British Petroleum (BP), one of the world's biggest oil companies that could become even bigger if a merger with Amoco is approved, announced on September 18 that it will cut its emissions of greenhouse gases by 10% from a 1990 baseline of 40 million tons of carbon dioxide between now and the year 2010.The target, which is double the amount of emissions reductions that industrialized nations agreed to under the Kyoto protocol on climate change, will now stand next to BP's financial targets, said John Browne, group chief executive of BP.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

PubMed

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Delineation of Rupture Propagation of Large Earthquakes Using Source-Scanning Algorithm: A Control Study

NASA Astrophysics Data System (ADS)

Kao, H.; Shan, S.

2004-12-01

Determination of the rupture propagation of large earthquakes is important and of wide interest to the seismological research community. The conventional inversion method determines the distribution of slip at a grid of subfaults whose orientations are predefined. As a result, difference choices of fault geometry and dimensions often result in different solutions. In this study, we try to reconstruct the rupture history of an earthquake using the newly developed Source-Scanning Algorithm (SSA) without imposing any a priori constraints on the fault's orientation and dimension. The SSA identifies the distribution of seismic sources in two steps. First, it calculates the theoretical arrival times from all grid points inside the model space to all seismic stations by assuming an origin time. Then, the absolute amplitudes of the observed waveforms at the predicted arrival times are added to give the "brightness" of each time-space pair, and the brightest spots mark the locations of sources. The propagation of the rupture is depicted by the migration of the brightest spots throughout a prescribed time window. A series of experiments are conducted to test the resolution of the SSA inversion. Contrary to the conventional wisdom that seismometers should be placed as close as possible to the fault trace to give the best resolution in delineating rupture details, we found that the best results are obtained if the seismograms are recorded at a distance about half of the total rupture length away from the fault trace. This is especially true when the rupture duration is longer than ~10 s. A possible explanation is that the geometric spreading effects for waveforms from different segments of the rupture are about the same if the stations are sufficiently away from the fault trace, thus giving a uniform resolution to the entire rupture history.

The accuracy of dynamic attitude propagation

NASA Technical Reports Server (NTRS)

Harvie, E.; Chu, D.; Woodard, M.

1990-01-01

Propagating attitude by integrating Euler's equation for rigid body motion has long been suggested for the Earth Radiation Budget Satellite (ERBS) but until now has not been implemented. Because of limited Sun visibility, propagation is necessary for yaw determination. With the deterioration of the gyros, dynamic propagation has become more attractive. Angular rates are derived from integrating Euler's equation with a stepsize of 1 second, using torques computed from telemetered control system data. The environmental torque model was quite basic. It included gravity gradient and unshadowed aerodynamic torques. Knowledge of control torques is critical to the accuracy of dynamic modeling. Due to their coarseness and sparsity, control actuator telemetry were smoothed before integration. The dynamic model was incorporated into existing ERBS attitude determination software. Modeled rates were then used for attitude propagation in the standard ERBS fine-attitude algorithm. In spite of the simplicity of the approach, the dynamically propagated attitude matched the attitude propagated with good gyros well for roll and yaw but diverged up to 3 degrees for pitch because of the very low resolution in pitch momentum wheel telemetry. When control anomalies significantly perturb the nominal attitude, the effect of telemetry granularity is reduced and the dynamically propagated attitudes are accurate on all three axes.

Use of a porous material description of forests in infrasonic propagation algorithms.

PubMed

Swearingen, Michelle E; White, Michael J; Ketcham, Stephen A; McKenna, Mihan H

2013-10-01

Infrasound can propagate very long distances and remain at measurable levels. As a result infrasound sensing is used for remote monitoring in many applications. At local ranges, on the order of 10 km, the influence of the presence or absence of forests on the propagation of infrasonic signals is considered. Because the wavelengths of interest are much larger than the scale of individual components, the forest is modeled as a porous material. This approximation is developed starting with the relaxation model of porous materials. This representation is then incorporated into a Crank-Nicholson method parabolic equation solver to determine the relative impacts of the physical parameters of a forest (trunk size and basal area), the presence of gaps/trees in otherwise continuous forest/open terrain, and the effects of meteorology coupled with the porous layer. Finally, the simulations are compared to experimental data from a 10.9 kg blast propagated 14.5 km. Comparison to the experimental data shows that appropriate inclusion of a forest layer along the propagation path provides a closer fit to the data than solely changing the ground type across the frequency range from 1 to 30 Hz.

Wave Propagation, Scattering and Imaging Using Dual-domain One-way and One-return Propagators

NASA Astrophysics Data System (ADS)

Wu, R.-S.

- Dual-domain one-way propagators implement wave propagation in heterogeneous media in mixed domains (space-wavenumber domains). One-way propagators neglect wave reverberations between heterogeneities but correctly handle the forward multiple-scattering including focusing/defocusing, diffraction, refraction and interference of waves. The algorithm shuttles between space-domain and wavenumber-domain using FFT, and the operations in the two domains are self-adaptive to the complexity of the media. The method makes the best use of the operations in each domain, resulting in efficient and accurate propagators. Due to recent progress, new versions of dual-domain methods overcame some limitations of the classical dual-domain methods (phase-screen or split-step Fourier methods) and can propagate large-angle waves quite accurately in media with strong velocity contrasts. These methods can deliver superior image quality (high resolution/high fidelity) for complex subsurface structures. One-way and one-return (De Wolf approximation) propagators can be also applied to wave-field modeling and simulations for some geophysical problems. In the article, a historical review and theoretical analysis of the Born, Rytov, and De Wolf approximations are given. A review on classical phase-screen or split-step Fourier methods is also given, followed by a summary and analysis of the new dual-domain propagators. The applications of the new propagators to seismic imaging and modeling are reviewed with several examples. For seismic imaging, the advantages and limitations of the traditional Kirchhoff migration and time-space domain finite-difference migration, when applied to 3-D complicated structures, are first analyzed. Then the special features, and applications of the new dual-domain methods are presented. Three versions of GSP (generalized screen propagators), the hybrid pseudo-screen, the wide-angle Padé-screen, and the higher-order generalized screen propagators are discussed. Recent

Nanoscopic exclusion between Rad51 and 53BP1 after ion irradiation in human HeLa cells

NASA Astrophysics Data System (ADS)

Reindl, Judith; Drexler, Guido A.; Girst, Stefanie; Greubel, Christoph; Siebenwirth, Christian; Drexler, Sophie E.; Dollinger, Günther; Friedl, Anna A.

2015-12-01

Many proteins involved in detection, signalling and repair of DNA double-strand breaks (DSB) accumulate in large number in the vicinity of DSB sites, forming so called foci. Emerging evidence suggests that these foci are sub-divided in structural or functional domains. We use stimulated emission depletion (STED) microscopy to investigate localization of mediator protein 53BP1 and recombination factor Rad51 after irradiation of cells with low linear energy transfer (LET) protons or high LET carbon ions. With a resolution better than 100 nm, STED microscopy and image analysis using a newly developed analyzing algorithm, the reduced product of the differences from the mean, allowed us to demonstrate that with both irradiation types Rad51 occupies spherical regions of about 200 nm diameter. These foci locate within larger 53BP1 accumulations in regions of local 53BP1 depletion, similar to what has been described for the localization of Brca1, CtIP and RPA. Furthermore, localization relative to 53BP1 and size of Rad51 foci was not different after irradiation with low and high LET radiation. As expected, 53BP1 foci induced by low LET irradiation mostly contained one Rad51 focal structure, while after high LET irradiation, most foci contained >1 Rad51 accumulation.

Equalization enhanced phase noise in Nyquist-spaced superchannel transmission systems using multi-channel digital back-propagation

PubMed Central

Xu, Tianhua; Liga, Gabriele; Lavery, Domaniç; Thomsen, Benn C.; Savory, Seb J.; Killey, Robert I.; Bayvel, Polina

2015-01-01

Superchannel transmission spaced at the symbol rate, known as Nyquist spacing, has been demonstrated for effectively maximizing the optical communication channel capacity and spectral efficiency. However, the achievable capacity and reach of transmission systems using advanced modulation formats are affected by fibre nonlinearities and equalization enhanced phase noise (EEPN). Fibre nonlinearities can be effectively compensated using digital back-propagation (DBP). However EEPN which arises from the interaction between laser phase noise and dispersion cannot be efficiently mitigated, and can significantly degrade the performance of transmission systems. Here we report the first investigation of the origin and the impact of EEPN in Nyquist-spaced superchannel system, employing electronic dispersion compensation (EDC) and multi-channel DBP (MC-DBP). Analysis was carried out in a Nyquist-spaced 9-channel 32-Gbaud DP-64QAM transmission system. Results confirm that EEPN significantly degrades the performance of all sub-channels of the superchannel system and that the distortions are more severe for the outer sub-channels, both using EDC and MC-DBP. It is also found that the origin of EEPN depends on the relative position between the carrier phase recovery module and the EDC (or MC-DBP) module. Considering EEPN, diverse coding techniques and modulation formats have to be applied for optimizing different sub-channels in superchannel systems. PMID:26365422

RanBP2 modulates Cox11 and hexokinase I activities and haploinsufficiency of RanBP2 causes deficits in glucose metabolism.

PubMed

Aslanukov, Azamat; Bhowmick, Reshma; Guruju, Mallikarjuna; Oswald, John; Raz, Dorit; Bush, Ronald A; Sieving, Paul A; Lu, Xinrong; Bock, Cheryl B; Ferreira, Paulo A

2006-10-01

The Ran-binding protein 2 (RanBP2) is a large multimodular and pleiotropic protein. Several molecular partners with distinct functions interacting specifically with selective modules of RanBP2 have been identified. Yet, the significance of these interactions with RanBP2 and the genetic and physiological role(s) of RanBP2 in a whole-animal model remain elusive. Here, we report the identification of two novel partners of RanBP2 and a novel physiological role of RanBP2 in a mouse model. RanBP2 associates in vitro and in vivo and colocalizes with the mitochondrial metallochaperone, Cox11, and the pacemaker of glycolysis, hexokinase type I (HKI) via its leucine-rich domain. The leucine-rich domain of RanBP2 also exhibits strong chaperone activity toward intermediate and mature folding species of Cox11 supporting a chaperone role of RanBP2 in the cytosol during Cox11 biogenesis. Cox11 partially colocalizes with HKI, thus supporting additional and distinct roles in cell function. Cox11 is a strong inhibitor of HKI, and RanBP2 suppresses the inhibitory activity of Cox11 over HKI. To probe the physiological role of RanBP2 and its role in HKI function, a mouse model harboring a genetically disrupted RanBP2 locus was generated. RanBP2(-/-) are embryonically lethal, and haploinsufficiency of RanBP2 in an inbred strain causes a pronounced decrease of HKI and ATP levels selectively in the central nervous system. Inbred RanBP2(+/-) mice also exhibit deficits in growth rates and glucose catabolism without impairment of glucose uptake and gluconeogenesis. These phenotypes are accompanied by a decrease in the electrophysiological responses of photosensory and postreceptoral neurons. Hence, RanBP2 and its partners emerge as critical modulators of neuronal HKI, glucose catabolism, energy homeostasis, and targets for metabolic, aging disorders and allied neuropathies.

Prevalence and management of back pain in adolescent idiopathic scoliosis patients: A retrospective study

PubMed Central

Théroux, Jean; Le May, Sylvie; Fortin, Carole; Labelle, Hubert

2015-01-01

BACKGROUND: Back pain (BP) has often been associated with adolescent idiopathic scoliosis (AIS), which is a three-dimensional deviation of the vertebral column. In adolescents, chronic pain appears to be a predictor of health care utilization and has a negative impact on physical, psychological and family well-being. In this population, BP tends to be persistent and may be a predictor of BP in adulthood. OBJECTIVE: To document the prevalence and management of BP in AIS patients. METHODS: A retrospective chart review of AIS patients who were referred to Sainte-Justine University Teaching Hospital (Montreal, Quebec) from 2006 to 2011 was conducted. RESULTS: A total of 310 randomly selected charts were reviewed. Nearly one-half of the patients (47.3%) mentioned that they experienced BP, most commonly in the lumbar (19.7%) and thoracic regions (7.7%). The type of BP was documented in only 36% (n=112) of the charts. Pain intensity was specified in only 21% (n=65) of the charts. In approximately 80% (n=248) of the charts, no pain management treatment plan was documented. CONCLUSIONS: The prevalence of BP was moderately high among the present sample of adolescents with AIS. An improved system for documenting BP assessment, type, treatment plan and treatment effectiveness would improve pain management for these patients. PMID:25831076

A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

PubMed

Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

2017-05-18

The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

Iris double recognition based on modified evolutionary neural network

NASA Astrophysics Data System (ADS)

Liu, Shuai; Liu, Yuan-Ning; Zhu, Xiao-Dong; Huo, Guang; Liu, Wen-Tao; Feng, Jia-Kai

2017-11-01

Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.

Back-Propagation of Physiological Action Potential Output in Dendrites of Slender-Tufted L5A Pyramidal Neurons

PubMed Central

Grewe, Benjamin F.; Bonnan, Audrey; Frick, Andreas

2009-01-01

Pyramidal neurons of layer 5A are a major neocortical output type and clearly distinguished from layer 5B pyramidal neurons with respect to morphology, in vivo firing patterns, and connectivity; yet knowledge of their dendritic properties is scant. We used a combination of whole-cell recordings and Ca2+ imaging techniques in vitro to explore the specific dendritic signaling role of physiological action potential patterns recorded in vivo in layer 5A pyramidal neurons of the whisker-related ‘barrel cortex’. Our data provide evidence that the temporal structure of physiological action potential patterns is crucial for an effective invasion of the main apical dendrites up to the major branch point. Both the critical frequency enabling action potential trains to invade efficiently and the dendritic calcium profile changed during postnatal development. In contrast to the main apical dendrite, the more passive properties of the short basal and apical tuft dendrites prevented an efficient back-propagation. Various Ca2+ channel types contributed to the enhanced calcium signals during high-frequency firing activity, whereas A-type K+ and BKCa channels strongly suppressed it. Our data support models in which the interaction of synaptic input with action potential output is a function of the timing, rate and pattern of action potentials, and dendritic location. PMID:20508744

Development of hybrid genetic-algorithm-based neural networks using regression trees for modeling air quality inside a public transportation bus.

PubMed

Kadiyala, Akhil; Kaur, Devinder; Kumar, Ashok

2013-02-01

The present study developed a novel approach to modeling indoor air quality (IAQ) of a public transportation bus by the development of hybrid genetic-algorithm-based neural networks (also known as evolutionary neural networks) with input variables optimized from using the regression trees, referred as the GART approach. This study validated the applicability of the GART modeling approach in solving complex nonlinear systems by accurately predicting the monitored contaminants of carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), sulfur dioxide (SO2), 0.3-0.4 microm sized particle numbers, 0.4-0.5 microm sized particle numbers, particulate matter (PM) concentrations less than 1.0 microm (PM10), and PM concentrations less than 2.5 microm (PM2.5) inside a public transportation bus operating on 20% grade biodiesel in Toledo, OH. First, the important variables affecting each monitored in-bus contaminant were determined using regression trees. Second, the analysis of variance was used as a complimentary sensitivity analysis to the regression tree results to determine a subset of statistically significant variables affecting each monitored in-bus contaminant. Finally, the identified subsets of statistically significant variables were used as inputs to develop three artificial neural network (ANN) models. The models developed were regression tree-based back-propagation network (BPN-RT), regression tree-based radial basis function network (RBFN-RT), and GART models. Performance measures were used to validate the predictive capacity of the developed IAQ models. The results from this approach were compared with the results obtained from using a theoretical approach and a generalized practicable approach to modeling IAQ that included the consideration of additional independent variables when developing the aforementioned ANN models. The hybrid GART models were able to capture majority of the variance in the monitored in-bus contaminants. The genetic-algorithm

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

NASA Astrophysics Data System (ADS)

Wang, Baocheng; Qu, Dandan; Tian, Qing; Pang, Liping

2018-05-01

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system (OFPS) is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform (MT) is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation (BP) neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Intelligent switching between different noise propagation algorithms: analysis and sensitivity

DOT National Transportation Integrated Search

2012-08-10

When modeling aircraft noise on a large scale (such as an analysis of annual aircraft : operations at an airport), it is important that the noise propagation model used for the : analysis be both efficient and accurate. In this analysis, three differ...

Evaluation of factors associated with severe and frequent back pain in high school athletes.

PubMed

Noll, Matias; Silveira, Erika Aparecida; Avelar, Ivan Silveira de

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14-20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01-1.33), posture while writing (PR: 1.41, CI: 1.27-1.58), and posture while using a computer (PR: 1.39, CI: 1.26-1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01-1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01-1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes.

Numerical algorithms for scatter-to-attenuation reconstruction in PET: empirical comparison of convergence, acceleration, and the effect of subsets.

PubMed

Berker, Yannick; Karp, Joel S; Schulz, Volkmar

2017-09-01

The use of scattered coincidences for attenuation correction of positron emission tomography (PET) data has recently been proposed. For practical applications, convergence speeds require further improvement, yet there exists a trade-off between convergence speed and the risk of non-convergence. In this respect, a maximum-likelihood gradient-ascent (MLGA) algorithm and a two-branch back-projection (2BP), which was previously proposed, were evaluated. MLGA was combined with the Armijo step size rule; and accelerated using conjugate gradients, Nesterov's momentum method, and data subsets of different sizes. In 2BP, we varied the subset size, an important determinant of convergence speed and computational burden. We used three sets of simulation data to evaluate the impact of a spatial scale factor. The Armijo step size allowed 10-fold increased step sizes compared to native MLGA. Conjugate gradients and Nesterov momentum lead to slightly faster, yet non-uniform convergence; improvements were mostly confined to later iterations, possibly due to the non-linearity of the problem. MLGA with data subsets achieved faster, uniform, and predictable convergence, with a speed-up factor equivalent to the number of subsets and no increase in computational burden. By contrast, 2BP computational burden increased linearly with the number of subsets due to repeated evaluation of the objective function, and convergence was limited to the case of many (and therefore small) subsets, which resulted in high computational burden. Possibilities of improving 2BP appear limited. While general-purpose acceleration methods appear insufficient for MLGA, results suggest that data subsets are a promising way of improving MLGA performance.

Improving BP control through electronic communications: an economic evaluation.

PubMed

Fishman, Paul A; Cook, Andrea J; Anderson, Melissa L; Ralston, James D; Catz, Sheryl L; Carrell, David; Carlson, James; Green, Beverly B

2013-09-01

Web-based collaborative approaches to managing chronic illness show promise for both improving health outcomes and increasing the efficiency of the healthcare system. Analyze the cost-effectiveness of the Electronic Communications and Home Blood Pressure Monitoring to Improve Blood Pressure Control (e-BP) study, a randomized controlled trial that used a patient-shared electronic medical record, home blood pressure (BP) monitoring, and web-based pharmacist care to improve BP control (<140/90 mm Hg). Incremental cost-effectiveness analysis conducted from a health plan perspective. Cost-effectiveness of home BP monitoring and web-based pharmacist care estimated for percent change in patients with controlled BP and cost per mm Hg in diastolic and systolic BP relative to usual care and home BP monitoring alone. A 1% improvement in number of patients with controlled BP using home BP monitoring and web-based pharmacist care-the e-BP program-costs $16.65 (95% confidence interval: 15.37- 17.94) relative to home BP monitoring and web training alone. Each mm HG reduction in systolic and diastolic BP achieved through the e-BP program costs $65.29 (59.91-70.67) relativeto home BP monitoring and web tools only. Life expectancy was increased at an incremental cost of $1850 (1635-2064) and $2220 (1745-2694) per year of life saved for men and women, respectively. Web-based collaborative care can be used to achieve BP control at a relatively low cost. Future research should examine the cost impact of potential long-term clinical improvements.

Unveiling the development of intracranial injury using dynamic brain EIT: an evaluation of current reconstruction algorithms.

PubMed

Li, Haoting; Chen, Rongqing; Xu, Canhua; Liu, Benyuan; Tang, Mengxing; Yang, Lin; Dong, Xiuzhen; Fu, Feng

2017-08-21

Dynamic brain electrical impedance tomography (EIT) is a promising technique for continuously monitoring the development of cerebral injury. While there are many reconstruction algorithms available for brain EIT, there is still a lack of study to compare their performance in the context of dynamic brain monitoring. To address this problem, we develop a framework for evaluating different current algorithms with their ability to correctly identify small intracranial conductivity changes. Firstly, a simulation 3D head phantom with realistic layered structure and impedance distribution is developed. Next several reconstructing algorithms, such as back projection (BP), damped least-square (DLS), Bayesian, split Bregman (SB) and GREIT are introduced. We investigate their temporal response, noise performance, location and shape error with respect to different noise levels on the simulation phantom. The results show that the SB algorithm demonstrates superior performance in reducing image error. To further improve the location accuracy, we optimize SB by incorporating the brain structure-based conductivity distribution priors, in which differences of the conductivities between different brain tissues and the inhomogeneous conductivity distribution of the skull are considered. We compare this novel algorithm (called SB-IBCD) with SB and DLS using anatomically correct head shaped phantoms with spatial varying skull conductivity. Main results and Significance: The results showed that SB-IBCD is the most effective in unveiling small intracranial conductivity changes, where it can reduce the image error by an average of 30.0% compared to DLS.

The epidemiology of back pain and its relationship with depression, psychosis, anxiety, sleep disturbances, and stress sensitivity: Data from 43 low- and middle-income countries.

PubMed

Stubbs, Brendon; Koyanagi, Ai; Thompson, Trevor; Veronese, Nicola; Carvalho, Andre F; Solomi, Marco; Mugisha, James; Schofield, Patricia; Cosco, Theodore; Wilson, Nicky; Vancampfort, Davy

Back pain (BP) is a leading cause of global disability. However, population-based studies investigating its impact on mental health outcomes are lacking, particularly among low- and middle-income countries (LMICs). Thus, the primary aims of this study were to: (1) determine the epidemiology of BP in 43 LMICs; (2) explore the relationship between BP and mental health (depression spectrum, psychosis spectrum, anxiety, sleep disturbances and stress). Data on 190,593 community-dwelling adults aged ≥18 years from the World Health Survey (WHS) 2002-2004 were analyzed. The presence of past-12 month psychotic symptoms and depression was established using questions from the Composite International Diagnostic Interview. Anxiety, sleep problems, stress sensitivity, and any BP or chronic BP (CBP) during the previous 30 days were also self-reported. Multivariable logistic regression analyses were undertaken. The overall prevalence of any BP and CBP were 35.1% and 6.9% respectively. Significant associations with any BP were observed for subsyndromal depression [OR (odds ratio)=2.21], brief depressive episode (OR=2.64), depressive episode (OR=2.88), psychosis diagnosis with symptoms (OR=2.05), anxiety (OR=2.12), sleep disturbance (OR=2.37) and the continuous variable of stress sensitivity. Associations were generally more pronounced for chronic BP. Our data establish that BP is associated with elevated mental health comorbidity in LMICs. Integrated interventions that address back pain and metal health comorbidities might be an important next step to tackle this considerable burden. Copyright © 2016 Elsevier Inc. All rights reserved.

Synaptic depolarization is more effective than back-propagating action potentials during induction of associative long-term potentiation in hippocampal pyramidal neurons.

PubMed

Hardie, Jason; Spruston, Nelson

2009-03-11

Long-term potentiation (LTP) requires postsynaptic depolarization that can result from EPSPs paired with action potentials or larger EPSPs that trigger dendritic spikes. We explored the relative contribution of these sources of depolarization to LTP induction during synaptically driven action potential firing in hippocampal CA1 pyramidal neurons. Pairing of a weak test input with a strong input resulted in large LTP (approximately 75% increase) when the weak and strong inputs were both located in the apical dendrites. This form of LTP did not require somatic action potentials. When the strong input was located in the basal dendrites, the resulting LTP was smaller (< or =25% increase). Pairing the test input with somatically evoked action potentials mimicked this form of LTP. Thus, back-propagating action potentials may contribute to modest LTP, but local synaptic depolarization and/or dendritic spikes mediate a stronger form of LTP that requires spatial proximity of the associated synaptic inputs.

A generalized LSTM-like training algorithm for second-order recurrent neural networks

PubMed Central

Monner, Derek; Reggia, James A.

2011-01-01

The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

Ran-binding protein 5 (RanBP5) is related to the nuclear transport factor importin-beta but interacts differently with RanBP1.

PubMed Central

Deane, R; Schäfer, W; Zimmermann, H P; Mueller, L; Görlich, D; Prehn, S; Ponstingl, H; Bischoff, F R

1997-01-01

We report the identification and characterization of a novel 124-kDa Ran binding protein, RanBP5. This protein is related to importin-beta, the key mediator of nuclear localization signal (NLS)-dependent nuclear transport. RanBP5 was identified by two independent methods: it was isolated from HeLa cells by using its interaction with RanGTP in an overlay assay to monitor enrichment, and it was also found by the yeast two-hybrid selection method with RanBP1 as bait. RanBP5 binds to RanBP1 as part of a trimeric RanBP1-Ran-RanBP5 complex. Like importin-beta, RanBP5 strongly binds the GTP-bound form of Ran, stabilizing it against both intrinsic and RanGAP1-induced GTP hydrolysis and also against nucleotide exchange. The GAP resistance of the RanBP5-RanGTP complex can be relieved by RanBP1, which might reflect an in vivo role for RanBP1. RanBP5 is a predominantly cytoplasmic protein that can bind to nuclear pore complexes. We propose that RanBP5 is a mediator of a nucleocytoplasmic transport pathway that is distinct from the importin-alpha-dependent import of proteins with a classical NLS. PMID:9271386

Least-squares Legendre spectral element solutions to sound propagation problems.

PubMed

Lin, W H

2001-02-01

This paper presents a novel algorithm and numerical results of sound wave propagation. The method is based on a least-squares Legendre spectral element approach for spatial discretization and the Crank-Nicolson [Proc. Cambridge Philos. Soc. 43, 50-67 (1947)] and Adams-Bashforth [D. Gottlieb and S. A. Orszag, Numerical Analysis of Spectral Methods: Theory and Applications (CBMS-NSF Monograph, Siam 1977)] schemes for temporal discretization to solve the linearized acoustic field equations for sound propagation. Two types of NASA Computational Aeroacoustics (CAA) Workshop benchmark problems [ICASE/LaRC Workshop on Benchmark Problems in Computational Aeroacoustics, edited by J. C. Hardin, J. R. Ristorcelli, and C. K. W. Tam, NASA Conference Publication 3300, 1995a] are considered: a narrow Gaussian sound wave propagating in a one-dimensional space without flows, and the reflection of a two-dimensional acoustic pulse off a rigid wall in the presence of a uniform flow of Mach 0.5 in a semi-infinite space. The first problem was used to examine the numerical dispersion and dissipation characteristics of the proposed algorithm. The second problem was to demonstrate the capability of the algorithm in treating sound propagation in a flow. Comparisons were made of the computed results with analytical results and results obtained by other methods. It is shown that all results computed by the present method are in good agreement with the analytical solutions and results of the first problem agree very well with those predicted by other schemes.

Axial 3D region of interest reconstruction using weighted cone beam BPF/DBPF algorithm cascaded with adequately oriented orthogonal butterfly filtering

NASA Astrophysics Data System (ADS)

Tang, Shaojie; Tang, Xiangyang

2016-03-01

Axial cone beam (CB) computed tomography (CT) reconstruction is still the most desirable in clinical applications. As the potential candidates with analytic form for the task, the back projection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical and axial reconstruction from CB and fan beam projection data, respectively. These two algorithms have been heuristically extended for axial CB reconstruction via adoption of virtual PI-line segments. Unfortunately, however, streak artifacts are induced along the Hilbert filtering direction, since these algorithms are no longer accurate on the virtual PI-line segments. We have proposed to cascade the extended BPF/DBPF algorithm with orthogonal butterfly filtering for image reconstruction (namely axial CB-BPP/DBPF cascaded with orthogonal butterfly filtering), in which the orientation-specific artifacts caused by post-BP Hilbert transform can be eliminated, at a possible expense of losing the BPF/DBPF's capability of dealing with projection data truncation. Our preliminary results have shown that this is not the case in practice. Hence, in this work, we carry out an algorithmic analysis and experimental study to investigate the performance of the axial CB-BPP/DBPF cascaded with adequately oriented orthogonal butterfly filtering for three-dimensional (3D) reconstruction in region of interest (ROI).

Comparative Analysis of Soft Computing Models in Prediction of Bending Rigidity of Cotton Woven Fabrics

NASA Astrophysics Data System (ADS)

Guruprasad, R.; Behera, B. K.

2015-10-01

Quantitative prediction of fabric mechanical properties is an essential requirement for design engineering of textile and apparel products. In this work, the possibility of prediction of bending rigidity of cotton woven fabrics has been explored with the application of Artificial Neural Network (ANN) and two hybrid methodologies, namely Neuro-genetic modeling and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling. For this purpose, a set of cotton woven grey fabrics was desized, scoured and relaxed. The fabrics were then conditioned and tested for bending properties. With the database thus created, a neural network model was first developed using back propagation as the learning algorithm. The second model was developed by applying a hybrid learning strategy, in which genetic algorithm was first used as a learning algorithm to optimize the number of neurons and connection weights of the neural network. The Genetic algorithm optimized network structure was further allowed to learn using back propagation algorithm. In the third model, an ANFIS modeling approach was attempted to map the input-output data. The prediction performances of the models were compared and a sensitivity analysis was reported. The results show that the prediction by neuro-genetic and ANFIS models were better in comparison with that of back propagation neural network model.

Tunneling in BP-MoS2 heterostructure

NASA Astrophysics Data System (ADS)

Liu, Xiaochi; Qu, Deshun; Kim, Changsik; Ahmed, Faisal; Yoo, Won Jong

Tunnel field effect transistor (TFET) is considered to be a leading option for achieving SS <60 mV/dec. In this work, black phosphorus (BP) and molybdenum disulfide (MoS2) heterojunction devices are fabricated. We find that thin BP flake and MoS2 form normal p-n junctions, tunneling phenomena can be observed when BP thickness increases to certain level. PEO:CsClO4 is applied on the surface of the device together with a side gate electrode patterned together with source and drain electrodes. The Fermi level of MoS2 on top of BP layer can be modulated by the side gating, and this enables to vary the MoS2-BP tunnel diode property from off-state to on-state. Since tunneling is the working mechanism of MoS2-BP junction, and PEO:CsClO4\\ possesses ultra high dielectric constant and small equivalent oxide thickness (EOT), a low SS of 55 mV/dec is obtained from MoS2-BP TFET. This work was supported by the Global Research Laboratory and Global Frontier R&D Programs at the Center for Hybrid Interface Materials, both funded by the Ministry of Science, ICT & Future Planning via the National Research Foundation of Korea (NRF).

Propagation failures, breathing pulses, and backfiring in an excitable reaction-diffusion system.

PubMed

Manz, Niklas; Steinbock, Oliver

2006-09-01

We report results from experiments with a pseudo-one-dimensional Belousov-Zhabotinsky reaction that employs 1,4-cyclohexanedione as its organic substrate. This excitable system shows traveling oxidation pulses and pulse trains that can undergo complex sequences of propagation failures. Moreover, we present examples for (i) breathing pulses that undergo periodic changes in speed and size and (ii) backfiring pulses that near their back repeatedly generate new pulses propagating in opposite direction.

Trailer siting issues: BP Texas City.

PubMed

Kaszniak, Mark; Holmstrom, Donald

2008-11-15

On 23 March, 2005, a series of explosions and fires occurred at the BP Texas City refinery during the startup of an isomerization (ISOM) process unit. Fifteen workers were killed and about 180 others were injured. All of the fatalities were contract workers; the deaths and most of the serious injuries occurred in and around temporary office trailers that had been sited near a blowdown drum and stack open to the atmosphere as part of ongoing turnaround activities in an adjacent unit. Due to problems that developed during the ISOM startup, flammable hydrocarbon liquid overfilled the blowdown drum and stack which resulted in a geyser-like release out the top into the atmosphere. The flammable hydrocarbons fell to the ground releasing vapors that were likely ignited from a nearby idling diesel pickup truck. A total of 44 trailers were damaged by the blast pressure wave that propagated through the refinery when the vapor cloud exploded. Thirteen trailers were totally destroyed and workers were injured in trailers as far as 479ft away from the release. The focus of this paper will be on trailer siting issues, including: need for work/office trailers within process units, adequacy of risk analysis methods in API RP 752, and minimum safe distance requirements

BP180 Is Critical in the Autoimmunity of Bullous Pemphigoid

PubMed Central

Liu, Yale; Li, Liang; Xia, Yumin

2017-01-01

Bullous pemphigoid (BP) is by far the most common autoimmune blistering dermatosis that mainly occurs in the elderly. The BP180 is a transmembrane glycoprotein, which is highly immunodominant in BP. The structure and location of BP180 indicate that it is a significant autoantigen and plays a key role in blister formation. Autoantibodies from BP patients react with BP180, which leads to its degradation and this has been regarded as the central event in BP pathogenesis. The consequent blister formation involves the activation of complement-dependent or -independent signals, as well as inflammatory pathways induced by BP180/anti-BP180 autoantibody interaction. As a multi-epitope molecule, BP180 can cause dermal–epidermal separation via combining each epitope with specific immunoglobulin, which also facilitates blister formation. In addition, some inflammatory factors can directly deplete BP180, thereby leading to fragility of the dermal–epidermal junction and blister formation. This review summarizes recent investigations on the role of BP180 in BP pathogenesis to determine the potential targets for the treatment of patients with BP. PMID:29276517

Educational inequality as a predictor of rising back pain prevalence in Austria-sex differences.

PubMed

Großschädl, Franziska; Stolz, Erwin; Mayerl, Hannes; Rásky, Éva; Freidl, Wolfgang; Stronegger, Willibald

2016-04-01

Back pain (BP) represents a widespread public health problem in Europe. The morbidity depends on several indicators, which must be investigated to discover risk groups. The examination of trends in socioeconomic developments should ensure a better understanding of the complex link between socioeconomic-status and BP. Therefore, the role of social inequalities for BP has been investigated among Austrian subpopulations over a 24-year period. Self-reported data from nationally representative health surveys (1983-2007) were analyzed and adjusted for self-report bias (N=121 486). Absolute changes (ACs) and aetiologic fractions (AF) were calculated to measure trends. To quantify the extent of social inequality, the relative index of inequality was computed based on educational levels. The prevalence of BP nearly doubled between 1983 and 2007. When investigating educational groups, subjects with low educational level were most prevalent. Obese persons generally showed higher rates of BP than non-obese subjects. Continuously rising trends across the different educational groups were more evident in men. The AC was highest in obese men with high education (+32.9%). Education-related inequalities for BP were more evident in men than women. Educational level is an important social indicator for BP. A gradient for low to high educational level in the trends of BP prevalence was clearly identified and stable only among men. We presume that the association 'education' and 'physical workload leading to BP' is more relevant for men than for women. The implementation of effective approaches to BP, in combination with target group-specific interventions focusing on educational status, is recommended. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

2D model of plasma current sheath propagation in a Mather type plasma focus device

NASA Astrophysics Data System (ADS)

Mohamad, Saiful Najmee; Rashid, Natashah Abdul; Halim, Mohd Mahadi; Ali, Jalil

2018-06-01

Plasma focus device is initially developed by two known researchers back in the 1960s, Mather and Filippov. The interest on the research built due to its capability to produce high energetic neutron from a fusion reaction. The relevance of the research in Plasma Focus device remain after decade is because of its competence to produce multi radiation yield and its known physics during nanosecond of plasma compression remain open for discussed. In the recent years, the direction of the plasma research is in device optimisation, where many possible configurations have been present, discuss and highlighting its performance for differences conditions. The significant difference between the electrode configuration is the profile of the dynamics inductance. In this context, this paper comparatively discusses the 1D dynamics model of the plasma current sheath (PSC) propagation axially and radially with the 2D model. The 2D model algorithm for the PSC propagation is developed using macro (Excel) by incorporating a drag force to solve the momentum exchange of the PCS with neutral gas. The discharge current profile of both model successfully calibrated to agree with each other with 2% difference at 1.83 µs after discharge but with an expense of different assumption.

Evaluation of factors associated with severe and frequent back pain in high school athletes

PubMed Central

Noll, Matias; Silveira, Erika Aparecida; de Avelar, Ivan Silveira

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14–20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01–1.33), posture while writing (PR: 1.41, CI: 1.27–1.58), and posture while using a computer (PR: 1.39, CI: 1.26–1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01–1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01–1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes. PMID:28222141

Prevalence of back pain in the community. A COPCORD-based study in the Mexican population.

PubMed

Peláez-Ballestas, Ingris; Flores-Camacho, Roxanna; Rodriguez-Amado, Jacqueline; Sanin, Luz Helena; Valerio, Jorge Esquivel; Navarro-Zarza, Eduardo; Flores, Diana; Rivas, Lourdes L; Casasola-Vargas, Julio; Burgos-Vargas, Ruben

2011-01-01

Back pain (BP) is frequent in the community; its prevalence in México is 6%. Our objective was to determine the prevalence of BP in Mexican communities and determine its most important characteristics. A cross-sectional study of individuals aged > 18 years was conducted in Mexico City and in urban communities in the state of Nuevo León. Sampling in Mexico City was based on community census and in Nuevo León, on stratified, balanced, and random sampling. Procedures included a door-to-door survey, using the Community Oriented Program for the Control of Rheumatic Diseases, to identify individuals with BP > 1 on a visual analog scale in the last 7 days. General practitioners/rheumatology fellows confirmed and characterized BP symptoms. In all, 8159 individuals (mean age 43.7 yrs, two-thirds female) were surveyed and 1219 had BP. The prevalence of nontraumatic BP in the last 7 days was 8.0% (95% CI 7.5-8.7). The mean age of these individuals was 42.7 years, and 61.9% were female. Thirty-seven percent had inflammatory BP [prevalence of 3.0% (95% CI 2.7-3.4)]. Compared with the state of Nuevo Léon, the characteristics and consequences of BP in Mexico City were more severe. In logistic regression analysis, living in Mexico City, having a paid job, any kind of musculoskeletal pain, high pain intensity, and obesity among other variables were associated with BP. The prevalence of nontraumatic BP in the last 7 days in urban communities in México is 8.0%. However, clinical features and consequences differed among the communities studied, suggesting a role for local factors in BP.

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

BP180 dysfunction triggers spontaneous skin inflammation in mice.

PubMed

Zhang, Yang; Hwang, Bin-Jin; Liu, Zhen; Li, Ning; Lough, Kendall; Williams, Scott E; Chen, Jinbo; Burette, Susan W; Diaz, Luis A; Su, Maureen A; Xiao, Shengxiang; Liu, Zhi

2018-06-04

BP180, also known as collagen XVII, is a hemidesmosomal component and plays a key role in maintaining skin dermal/epidermal adhesion. Dysfunction of BP180, either through genetic mutations in junctional epidermolysis bullosa (JEB) or autoantibody insult in bullous pemphigoid (BP), leads to subepidermal blistering accompanied by skin inflammation. However, whether BP180 is involved in skin inflammation remains unknown. To address this question, we generated a BP180-dysfunctional mouse strain and found that mice lacking functional BP180 (termed Δ NC16A ) developed spontaneous skin inflammatory disease, characterized by severe itch, defective skin barrier, infiltrating immune cells, elevated serum IgE levels, and increased expression of thymic stromal lymphopoietin (TSLP). Severe itch is independent of adaptive immunity and histamine, but dependent on increased expression of TSLP by keratinocytes. In addition, a high TSLP expression is detected in BP patients. Our data provide direct evidence showing that BP180 regulates skin inflammation independently of adaptive immunity, and BP180 dysfunction leads to a TSLP-mediated itch. The newly developed mouse strain could be a model for elucidation of disease mechanisms and development of novel therapeutic strategies for skin inflammation and BP180-related skin conditions.

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Liu, Jianjun; Kan, Jianquan

2018-04-01

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Power of automated algorithms for combining time-line follow-back and urine drug screening test results in stimulant-abuse clinical trials.

PubMed

Oden, Neal L; VanVeldhuisen, Paul C; Wakim, Paul G; Trivedi, Madhukar H; Somoza, Eugene; Lewis, Daniel

2011-09-01

In clinical trials of treatment for stimulant abuse, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. To compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. We performed Monte Carlo simulations patterned in part on real data to model self-report reliability, UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use. Further investigation is needed to determine if simple, naïve procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials (NIDA. NIDA Monograph-57: Self-Report Methods of Estimating Drug Abuse: Meeting Current Challenges to Validity. NTIS PB 88248083. Bethesda, MD: National Institutes of Health, 1985; NIDA. NIDA Research Monograph 73: Urine Testing for Drugs of Abuse. NTIS PB 89151971. Bethesda, MD: National Institutes of Health, 1987; NIDA. NIDA Research

Propagation of back-arc extension in the arc of the southern New Hebrides Subduction Zone (South West Pacific) and possible relation to subduction initiation.

NASA Astrophysics Data System (ADS)

Fabre, M.; Patriat, M.; Collot, J.; Danyushevsky, L. V.; Meffre, S.; Falloon, T.; Rouillard, P.; Pelletier, B.; Roach, M. J.; Fournier, M.

2015-12-01

Geophysical data acquired during three expeditions of the R/V Southern Surveyor allows us to characterize the deformation of the upper plate at the southern termination of the New Hebrides subduction zone where it bends 90° eastward along the Hunter Ridge. As shown by GPS measurements and earthquake slip vectors systematically orthogonal to the trench, this 90° bend does not mark a transition from subduction to strike slip as usually observed at subduction termination. Here the convergence direction remains continuously orthogonal to the trench notwithstanding its bend. Multibeam bathymetric data acquired in the North Fiji Basin reveals active deformation and fragmentation of the upper plate. It shows the southward propagation of a N-S back-arc spreading ridge into the pre-existing volcanic arc, and the connection of the southern end of the spreading axis with an oblique active rift in the active arc. Ultimately the active arc lithosphere is sheared as spreading progressively supersedes rifting. Consequently to such incursion of back-arc basin extension into the arc, peeled off and drifted pieces of arc crust are progressively isolated into the back-arc basin. Another consequence is that the New Hebrides arc is split in two distinct microplates, which move independently relative to the lower plate, and thereby define two different subduction systems. We suggest arc fragmentation could be a consequence of the incipient collision of the Loyalty Ridge with the New Hebrides Arc. We further speculate that this kinematic change could have resulted, less than two million year ago, in the initiation of a new subduction orthogonal to the New Hebrides Subduction possibly along the paleo STEP fault. In this geodynamic setting, with an oceanic lithosphere subducting beneath a sheared volcanic arc, a particularly wide range of primitive subduction-related magmas have been produced including adakites, island arc tholeiites, back-arc basin basalts, and medium-K subduction

Validation of a new algorithm for the BPM-100 electronic oscillometric office blood pressure monitor.

PubMed

Wright, J M; Mattu, G S; Perry, T L; Gelferc, M E; Strange, K D; Zorn, A; Chen, Y

2001-06-01

To test the accuracy of a new algorithm for the BPM-100, an automated oscillometric blood pressure (BP) monitor, using stored data from an independently conducted validation trial comparing the BPM-100(Beta) with a mercury sphygmomanometer. Raw pulse wave and cuff pressure data were stored electronically using embedded software in the BPM-100(Beta), during the validation trial. The 391 sets of measurements were separated objectively into two subsets. A subset of 136 measurements was used to develop a new algorithm to enhance the accuracy of the device when reading higher systolic pressures. The larger subset of 255 measurements (three readings for 85 subjects) was used as test data to validate the accuracy of the new algorithm. Differences between the new algorithm BPM-100 and the reference (mean of two observers) were determined and expressed as the mean difference +/- SD, plus the percentage of measurements within 5, 10, and 15 mmHg. The mean difference between the BPM-100 and reference systolic BP was -0.16 +/- 5.13 mmHg, with 73.7% < or = 5 mmHg, 94.9% < or = 10 mmHg and 98.8% < or = 15 mmHg. The mean difference between the BPM-100 and reference diastolic BP was -1.41 +/- 4.67 mmHg, with 78.4% < or = 5 mmHg, 92.5% < or = 10 mmHg, and 99.2% < or = 15 mmHg. These data improve upon that of the BPM-100(Beta) and pass the AAMI standard, and 'A' grade BHS protocol. This study illustrates a new method for developing and testing a change in an algorithm for an oscillometric BP monitor utilizing collected and stored electronic data and demonstrates that the new algorithm meets the AAMI standard and BHS protocol.

BP Reg Experiment Operations

NASA Image and Video Library

2015-04-07

ISS043E091755 (04/07/2015) --- Expedition 43 Commander Terry Virts is seen here working inside of the Columbus laboratory on the Blood Pressure Regulation (BP Reg) experiment. Astronauts returning from long-duration space flights risk experiencing dizziness or fainting when they stand immediately after returning to Earth. This has an important health risk as it reduces the potential for astronauts to safely escape from an emergency situation. BP Reg will help researchers develop appropriate countermeasures so that astronauts returning from long-duration space flights will have very low risk of experiencing dizziness or fainting when they return to Earth.

BP Reg Experiment Operations

NASA Image and Video Library

2015-04-07

ISS043E091740 (04/07/2015) --- Expedition 43 Commander Terry Virts is seen here working inside of the Columbus laboratory on the Blood Pressure Regulation (BP Reg) experiment. Astronauts returning from long-duration space flights risk experiencing dizziness or fainting when they stand immediately after returning to Earth. This has an important health risk as it reduces the potential for astronauts to safely escape from an emergency situation. BP Reg will help researchers develop appropriate countermeasures so that astronauts returning from long-duration space flights will have very low risk of experiencing dizziness or fainting when they return to Earth.

The Temporal Morphology of Infrasound Propagation

NASA Astrophysics Data System (ADS)

Drob, Douglas P.; Garcés, Milton; Hedlin, Michael; Brachet, Nicolas

2010-05-01

Expert knowledge suggests that the performance of automated infrasound event association and source location algorithms could be greatly improved by the ability to continually update station travel-time curves to properly account for the hourly, daily, and seasonal changes of the atmospheric state. With the goal of reducing false alarm rates and improving network detection capability we endeavor to develop, validate, and integrate this capability into infrasound processing operations at the International Data Centre of the Comprehensive Nuclear Test-Ban Treaty Organization. Numerous studies have demonstrated that incorporation of hybrid ground-to-space (G2S) enviromental specifications in numerical calculations of infrasound signal travel time and azimuth deviation yields significantly improved results over that of climatological atmospheric specifications, specifically for tropospheric and stratospheric modes. A robust infrastructure currently exists to generate hybrid G2S vector spherical harmonic coefficients, based on existing operational and emperical models on a real-time basis (every 3- to 6-hours) (D rob et al., 2003). Thus the next requirement in this endeavor is to refine numerical procedures to calculate infrasound propagation characteristics for robust automatic infrasound arrival identification and network detection, location, and characterization algorithms. We present results from a new code that integrates the local (range-independent) τp ray equations to provide travel time, range, turning point, and azimuth deviation for any location on the globe given a G2S vector spherical harmonic coefficient set. The code employs an accurate numerical technique capable of handling square-root singularities. We investigate the seasonal variability of propagation characteristics over a five-year time series for two different stations within the International Monitoring System with the aim of understanding the capabilities of current working knowledge of the

Implicit level set algorithms for modelling hydraulic fracture propagation.

PubMed

Peirce, A

2016-10-13

Hydraulic fractures are tensile cracks that propagate in pre-stressed solid media due to the injection of a viscous fluid. Developing numerical schemes to model the propagation of these fractures is particularly challenging due to the degenerate, hypersingular nature of the coupled integro-partial differential equations. These equations typically involve a singular free boundary whose velocity can only be determined by evaluating a distinguished limit. This review paper describes a class of numerical schemes that have been developed to use the multiscale asymptotic behaviour typically encountered near the fracture boundary as multiple physical processes compete to determine the evolution of the fracture. The fundamental concepts of locating the free boundary using the tip asymptotics and imposing the tip asymptotic behaviour in a weak form are illustrated in two quite different formulations of the governing equations. These formulations are the displacement discontinuity boundary integral method and the extended finite-element method. Practical issues are also discussed, including new models for proppant transport able to capture 'tip screen-out'; efficient numerical schemes to solve the coupled nonlinear equations; and fast methods to solve resulting linear systems. Numerical examples are provided to illustrate the performance of the numerical schemes. We conclude the paper with open questions for further research. This article is part of the themed issue 'Energy and the subsurface'. © 2016 The Author(s).

Implicit level set algorithms for modelling hydraulic fracture propagation

PubMed Central

2016-01-01

Hydraulic fractures are tensile cracks that propagate in pre-stressed solid media due to the injection of a viscous fluid. Developing numerical schemes to model the propagation of these fractures is particularly challenging due to the degenerate, hypersingular nature of the coupled integro-partial differential equations. These equations typically involve a singular free boundary whose velocity can only be determined by evaluating a distinguished limit. This review paper describes a class of numerical schemes that have been developed to use the multiscale asymptotic behaviour typically encountered near the fracture boundary as multiple physical processes compete to determine the evolution of the fracture. The fundamental concepts of locating the free boundary using the tip asymptotics and imposing the tip asymptotic behaviour in a weak form are illustrated in two quite different formulations of the governing equations. These formulations are the displacement discontinuity boundary integral method and the extended finite-element method. Practical issues are also discussed, including new models for proppant transport able to capture ‘tip screen-out’; efficient numerical schemes to solve the coupled nonlinear equations; and fast methods to solve resulting linear systems. Numerical examples are provided to illustrate the performance of the numerical schemes. We conclude the paper with open questions for further research.  This article is part of the themed issue ‘Energy and the subsurface’. PMID:27597787

Measuring contraction propagation and localizing pacemaker cells using high speed video microscopy

PubMed Central

Akl, Tony J.; Nepiyushchikh, Zhanna V.; Gashev, Anatoliy A.; Zawieja, David C.; Coté, Gerard L.

2011-01-01

Previous studies have shown the ability of many lymphatic vessels to contract phasically to pump lymph. Every lymphangion can act like a heart with pacemaker sites that initiate the phasic contractions. The contractile wave propagates along the vessel to synchronize the contraction. However, determining the location of the pacemaker sites within these vessels has proven to be very difficult. A high speed video microscopy system with an automated algorithm to detect pacemaker location and calculate the propagation velocity, speed, duration, and frequency of the contractions is presented in this paper. Previous methods for determining the contractile wave propagation velocity manually were time consuming and subject to errors and potential bias. The presented algorithm is semiautomated giving objective results based on predefined criteria with the option of user intervention. The system was first tested on simulation images and then on images acquired from isolated microlymphatic mesenteric vessels. We recorded contraction propagation velocities around 10 mm∕s with a shortening speed of 20.4 to 27.1 μm∕s on average and a contraction frequency of 7.4 to 21.6 contractions∕min. The simulation results showed that the algorithm has no systematic error when compared to manual tracking. The system was used to determine the pacemaker location with a precision of 28 μm when using a frame rate of 300 frames per second. PMID:21361700

A Two-Dimensional Flow Sensor with Integrated Micro Thermal Sensing Elements and a Back Propagation Neural Network

PubMed Central

Que, Ruiyi; Zhu, Rong

2014-01-01

This paper demonstrates a novel flow sensor with two-dimensional 360° direction sensitivity achieved with a simple structure and a novel data fusion algorithm. Four sensing elements with roundabout wires distributed in four quadrants of a circle compose the sensor probe, and work in constant temperature difference (CTD) mode as both Joule heaters and temperature detectors. The magnitude and direction of a fluid flow are measured by detecting flow-induced temperature differences among the four elements. The probe is made of Ti/Au thin-film with a diameter of 2 mm, and is fabricated using micromachining techniques. When a flow goes through the sensor, the flow-induced temperature differences are detected by the sensing elements that also serve as the heaters of the sensor. By measuring the temperature differences among the four sensing elements symmetrically distributed in the sensing area, a full 360° direction sensitivity can be obtained. By using a BP neural network to model the relationship between the readouts of the four sensor elements and flow parameters and execute data fusion, the magnitude and direction of the flow can be deduced. Validity of the sensor design was proven through both simulations and experiments. Wind tunnel experimental results show that the measurement accuracy of the airflow speed reaches 0.72 m/s in the range of 3 m/s–30 m/s and the measurement accuracy of flow direction angle reaches 1.9° in the range of 360°. PMID:24385032

A two-dimensional flow sensor with integrated micro thermal sensing elements and a back propagation neural network.

PubMed

Que, Ruiyi; Zhu, Rong

2013-12-31

This paper demonstrates a novel flow sensor with two-dimensional 360° direction sensitivity achieved with a simple structure and a novel data fusion algorithm. Four sensing elements with roundabout wires distributed in four quadrants of a circle compose the sensor probe, and work in constant temperature difference (CTD) mode as both Joule heaters and temperature detectors. The magnitude and direction of a fluid flow are measured by detecting flow-induced temperature differences among the four elements. The probe is made of Ti/Au thin-film with a diameter of 2 mm, and is fabricated using micromachining techniques. When a flow goes through the sensor, the flow-induced temperature differences are detected by the sensing elements that also serve as the heaters of the sensor. By measuring the temperature differences among the four sensing elements symmetrically distributed in the sensing area, a full 360° direction sensitivity can be obtained. By using a BP neural network to model the relationship between the readouts of the four sensor elements and flow parameters and execute data fusion, the magnitude and direction of the flow can be deduced. Validity of the sensor design was proven through both simulations and experiments. Wind tunnel experimental results show that the measurement accuracy of the airflow speed reaches 0.72 m/s in the range of 3 m/s-30 m/s and the measurement accuracy of flow direction angle reaches 1.9° in the range of 360°.

Investigating the performance of wavelet neural networks in ionospheric tomography using IGS data over Europe

NASA Astrophysics Data System (ADS)

Ghaffari Razin, Mir Reza; Voosoghi, Behzad

2017-04-01

Ionospheric tomography is a very cost-effective method which is used frequently to modeling of electron density distributions. In this paper, residual minimization training neural network (RMTNN) is used in voxel based ionospheric tomography. Due to the use of wavelet neural network (WNN) with back-propagation (BP) algorithm in RMTNN method, the new method is named modified RMTNN (MRMTNN). To train the WNN with BP algorithm, two cost functions is defined: total and vertical cost functions. Using minimization of cost functions, temporal and spatial ionospheric variations is studied. The GPS measurements of the international GNSS service (IGS) in the central Europe have been used for constructing a 3-D image of the electron density. Three days (2009.04.15, 2011.07.20 and 2013.06.01) with different solar activity index is used for the processing. To validate and better assess reliability of the proposed method, 4 ionosonde and 3 testing stations have been used. Also the results of MRMTNN has been compared to that of the RMTNN method, international reference ionosphere model 2012 (IRI-2012) and spherical cap harmonic (SCH) method as a local ionospheric model. The comparison of MRMTNN results with RMTNN, IRI-2012 and SCH models shows that the root mean square error (RMSE) and standard deviation of the proposed approach are superior to those of the traditional method.

Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules.

PubMed

Cohen, Julien G; Kim, Hyungjin; Park, Su Bin; van Ginneken, Bram; Ferretti, Gilbert R; Lee, Chang Hyun; Goo, Jin Mo; Park, Chang Min

2017-08-01

To evaluate the differences between filtered back projection (FBP) and model-based iterative reconstruction (MBIR) algorithms on semi-automatic measurements in subsolid nodules (SSNs). Unenhanced CT scans of 73 SSNs obtained using the same protocol and reconstructed with both FBP and MBIR algorithms were evaluated by two radiologists. Diameter, mean attenuation, mass and volume of whole nodules and their solid components were measured. Intra- and interobserver variability and differences between FBP and MBIR were then evaluated using Bland-Altman method and Wilcoxon tests. Longest diameter, volume and mass of nodules and those of their solid components were significantly higher using MBIR (p < 0.05) with mean differences of 1.1% (limits of agreement, -6.4 to 8.5%), 3.2% (-20.9 to 27.3%) and 2.9% (-16.9 to 22.7%) and 3.2% (-20.5 to 27%), 6.3% (-51.9 to 64.6%), 6.6% (-50.1 to 63.3%), respectively. The limits of agreement between FBP and MBIR were within the range of intra- and interobserver variability for both algorithms with respect to the diameter, volume and mass of nodules and their solid components. There were no significant differences in intra- or interobserver variability between FBP and MBIR (p > 0.05). Semi-automatic measurements of SSNs significantly differed between FBP and MBIR; however, the differences were within the range of measurement variability. • Intra- and interobserver reproducibility of measurements did not differ between FBP and MBIR. • Differences in SSNs' semi-automatic measurement induced by reconstruction algorithms were not clinically significant. • Semi-automatic measurement may be conducted regardless of reconstruction algorithm. • SSNs' semi-automated classification agreement (pure vs. part-solid) did not significantly differ between algorithms.

Tumor propagation model using generalized hidden Markov model

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dustin

2017-02-01

Tumor tracking and progression analysis using medical images is a crucial task for physicians to provide accurate and efficient treatment plans, and monitor treatment response. Tumor progression is tracked by manual measurement of tumor growth performed by radiologists. Several methods have been proposed to automate these measurements with segmentation, but many current algorithms are confounded by attached organs and vessels. To address this problem, we present a new generalized tumor propagation model considering time-series prior images and local anatomical features using a Hierarchical Hidden Markov model (HMM) for tumor tracking. First, we apply the multi-atlas segmentation technique to identify organs/sub-organs using pre-labeled atlases. Second, we apply a semi-automatic direct 3D segmentation method to label the initial boundary between the lesion and neighboring structures. Third, we detect vessels in the ROI surrounding the lesion. Finally, we apply the propagation model with the labeled organs and vessels to accurately segment and measure the target lesion. The algorithm has been designed in a general way to be applicable to various body parts and modalities. In this paper, we evaluate the proposed algorithm on lung and lung nodule segmentation and tracking. We report the algorithm's performance by comparing the longest diameter and nodule volumes using the FDA lung Phantom data and a clinical dataset.

Parabolic equation for nonlinear acoustic wave propagation in inhomogeneous moving media

NASA Astrophysics Data System (ADS)

Aver'yanov, M. V.; Khokhlova, V. A.; Sapozhnikov, O. A.; Blanc-Benon, Ph.; Cleveland, R. O.

2006-12-01

A new parabolic equation is derived to describe the propagation of nonlinear sound waves in inhomogeneous moving media. The equation accounts for diffraction, nonlinearity, absorption, scalar inhomogeneities (density and sound speed), and vectorial inhomogeneities (flow). A numerical algorithm employed earlier to solve the KZK equation is adapted to this more general case. A two-dimensional version of the algorithm is used to investigate the propagation of nonlinear periodic waves in media with random inhomogeneities. For the case of scalar inhomogeneities, including the case of a flow parallel to the wave propagation direction, a complex acoustic field structure with multiple caustics is obtained. Inclusion of the transverse component of vectorial random inhomogeneities has little effect on the acoustic field. However, when a uniform transverse flow is present, the field structure is shifted without changing its morphology. The impact of nonlinearity is twofold: it produces strong shock waves in focal regions, while, outside the caustics, it produces higher harmonics without any shocks. When the intensity is averaged across the beam propagating through a random medium, it evolves similarly to the intensity of a plane nonlinear wave, indicating that the transverse redistribution of acoustic energy gives no considerable contribution to nonlinear absorption.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

PubMed

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-12-13

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

Evaluation of non-rigid constrained CT/CBCT registration algorithms for delineation propagation in the context of prostate cancer radiotherapy

NASA Astrophysics Data System (ADS)

Rubeaux, Mathieu; Simon, Antoine; Gnep, Khemara; Colliaux, Jérémy; Acosta, Oscar; de Crevoisier, Renaud; Haigron, Pascal

2013-03-01

Image-Guided Radiation Therapy (IGRT) aims at increasing the precision of radiation dose delivery. In the context of prostate cancer, a planning Computed Tomography (CT) image with manually defined prostate and organs at risk (OAR) delineations is usually associated with daily Cone Beam Computed Tomography (CBCT) follow-up images. The CBCT images allow to visualize the prostate position and to reposition the patient accordingly. They also should be used to evaluate the dose received by the organs at each fraction of the treatment. To do so, the first step is a prostate and OAR segmentation on the daily CBCTs, which is very timeconsuming. To simplify this task, CT to CBCT non-rigid registration could be used in order to propagate the original CT delineations in the CBCT images. For this aim, we compared several non-rigid registration methods. They are all based on the Mutual Information (MI) similarity measure, and use a BSpline transformation model. But we add different constraints to this global scheme in order to evaluate their impact on the final results. These algorithms are investigated on two real datasets, representing a total of 70 CBCT on which a reference delineation has been realized. The evaluation is led using the Dice Similarity Coefficient (DSC) as a quality criteria. The experiments show that a rigid penalty term on the bones improves the final registration result, providing high quality propagated delineations.

Super-Encryption Implementation Using Monoalphabetic Algorithm and XOR Algorithm for Data Security

NASA Astrophysics Data System (ADS)

Rachmawati, Dian; Andri Budiman, Mohammad; Aulia, Indra

2018-03-01

The exchange of data that occurs offline and online is very vulnerable to the threat of data theft. In general, cryptography is a science and art to maintain data secrecy. An encryption is a cryptography algorithm in which data is transformed into cipher text, which is something that is unreadable and meaningless so it cannot be read or understood by other parties. In super-encryption, two or more encryption algorithms are combined to make it more secure. In this work, Monoalphabetic algorithm and XOR algorithm are combined to form a super- encryption. Monoalphabetic algorithm works by changing a particular letter into a new letter based on existing keywords while the XOR algorithm works by using logic operation XOR Since Monoalphabetic algorithm is a classical cryptographic algorithm and XOR algorithm is a modern cryptographic algorithm, this scheme is expected to be both easy-to-implement and more secure. The combination of the two algorithms is capable of securing the data and restoring it back to its original form (plaintext), so the data integrity is still ensured.

A discrete search algorithm for finding the structure of protein backbones and side chains.

PubMed

Sallaume, Silas; Martins, Simone de Lima; Ochi, Luiz Satoru; Da Silva, Warley Gramacho; Lavor, Carlile; Liberti, Leo

2013-01-01

Some information about protein structure can be obtained by using Nuclear Magnetic Resonance (NMR) techniques, but they provide only a sparse set of distances between atoms in a protein. The Molecular Distance Geometry Problem (MDGP) consists in determining the three-dimensional structure of a molecule using a set of known distances between some atoms. Recently, a Branch and Prune (BP) algorithm was proposed to calculate the backbone of a protein, based on a discrete formulation for the MDGP. We present an extension of the BP algorithm that can calculate not only the protein backbone, but the whole three-dimensional structure of proteins.

Microdeletion/microduplication of proximal 15q11.2 between BP1 and BP2: a susceptibility region for neurological dysfunction including developmental and language delay.

PubMed

Burnside, Rachel D; Pasion, Romela; Mikhail, Fady M; Carroll, Andrew J; Robin, Nathaniel H; Youngs, Erin L; Gadi, Inder K; Keitges, Elizabeth; Jaswaney, Vikram L; Papenhausen, Peter R; Potluri, Venkateswara R; Risheg, Hiba; Rush, Brooke; Smith, Janice L; Schwartz, Stuart; Tepperberg, James H; Butler, Merlin G

2011-10-01

The proximal long arm of chromosome 15 has segmental duplications located at breakpoints BP1-BP5 that mediate the generation of NAHR-related microdeletions and microduplications. The classical Prader-Willi/Angelman syndrome deletion is flanked by either of the proximal BP1 or BP2 breakpoints and the distal BP3 breakpoint. The larger Type I deletions are flanked by BP1 and BP3 in both Prader-Willi and Angelman syndrome subjects. Those with this deletion are reported to have a more severe phenotype than individuals with either Type II deletions (BP2-BP3) or uniparental disomy 15. The BP1-BP2 region spans approximately 500 kb and contains four evolutionarily conserved genes that are not imprinted. Reports of mutations or disturbed expression of these genes appear to impact behavioral and neurological function in affected individuals. Recently, reports of deletions and duplications flanked by BP1 and BP2 suggest an association with speech and motor delays, behavioral problems, seizures, and autism. We present a large cohort of subjects with copy number alteration of BP1 to BP2 with common phenotypic features. These include autism, developmental delay, motor and language delays, and behavioral problems, which were present in both cytogenetic groups. Parental studies demonstrated phenotypically normal carriers in several instances, and mildly affected carriers in others, complicating phenotypic association and/or causality. Possible explanations for these results include reduced penetrance, altered gene dosage on a particular genetic background, or a susceptibility region as reported for other areas of the genome implicated in autism and behavior disturbances.

Profile of NF-κBp(65/NFκBp50) among prostate specific antigen sera levels in prostatic pathologies.

PubMed

Bouraoui, Y; Ben Jemaa, A; Rodriguez, G; Ben Rais, N; Fraile, B; Paniagua, R; Sellemi, S; Royuela, M; Oueslati, R

2012-10-01

The aim of this work was to characterise the immunoexpression of NF-κB (p50/p65) in human prostatic pathologies and to study its profiles of activation among sera prostate specific antigen antigen (PSA) according the three groups: 0-4ng/mL, 4-20ng/mL and >20ng/mL. Twenty-four men with benign prostate hyperplasia (BPH); 19 men with prostate cancer (PC) and five men with normal prostates (NP). Immunohistochemical and western blot analysis was performed. Serum levels of PSA were assayed by immulite autoanalyser. In BPH and PC samples, immunoexpressions were observed for NF-κBp65 and NF-κBp50; while in NP samples, only were detected NF-κBp50. PC samples showed immunoreactions to NF-κBp65 and NF-κBp50 more intense (respectively 24.18±0.67 and 28.23±2.01) than that observed in BPH samples (respectively18.46±2.04 and 18.66±1.59) with special localisation in the nucleus. Different profiles of NF-κBp65 immunoexpressions were observed and BPH patients with sera PSA levels between 0-4ng/mL presented a significant weak percentage compared to BPH patients with sera PSA levels between 4-20ng/mL and >20ng/mL. No immunoreactions to NF-κBp65 were observed in PC patients with sera PSA levels between 4-20ng/mL. The sensibility of both NF-κB and PSA to inflammation allowed confirming the relationship between these two molecules and its involvement in prostatic diseases progression (inflammatory and neoplasic). Copyright © 2011 Elsevier Masson SAS. All rights reserved.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation.

PubMed

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-03-11

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

PubMed Central

Zhang, Tao; Shi, Hongfei; Chen, Liping; Li, Yao; Tong, Jinwu

2016-01-01

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL. PMID:26978361

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management

The impact of the BP Baker report.

PubMed

Rodríguez, Jennifer M; Payne, Stephanie C; Bergman, Mindy E; Beus, Jeremy M

2011-06-01

This study examined the impact of the British Petroleum (BP) Baker Panel Report, reviewing the March 2005 BP-Texas City explosion, on the field of process safety. Three hundred eighty-four subscribers of a process safety listserv responded to a survey two years after the BP Baker Report was published. Results revealed respondents in the field of process safety are familiar with the BP Baker Report, feel it is important to the future safety of chemical processing, and believe that the findings are generalizable to other plants beyond BP-Texas City. Respondents indicated that few organizations have administered the publicly available BP Process Safety Culture Survey. Our results also showed that perceptions of contractors varied depending on whether respondents were part of processing organizations (internal perspective) or government or consulting agencies (external perspective). This research provides some insight into the beliefs of chemical processing personnel regarding the transportability and generalizability of lessons learned from one organization to another. This study has implications for both organizational scientists and engineers in that it reveals perceptions about the primary mechanism used to share lessons learned within one industry about one major catastrophe (i.e., investigation reports). This study provides preliminary information about the perceived impact of a report such as this one. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.

Information recovery in propagation-based imaging with decoherence effects

NASA Astrophysics Data System (ADS)

Froese, Heinrich; Lötgering, Lars; Wilhein, Thomas

2017-05-01

During the past decades the optical imaging community witnessed a rapid emergence of novel imaging modalities such as coherent diffraction imaging (CDI), propagation-based imaging and ptychography. These methods have been demonstrated to recover complex-valued scalar wave fields from redundant data without the need for refractive or diffractive optical elements. This renders these techniques suitable for imaging experiments with EUV and x-ray radiation, where the use of lenses is complicated by fabrication, photon efficiency and cost. However, decoherence effects can have detrimental effects on the reconstruction quality of the numerical algorithms involved. Here we demonstrate propagation-based optical phase retrieval from multiple near-field intensities with decoherence effects such as partially coherent illumination, detector point spread, binning and position uncertainties of the detector. Methods for overcoming these systematic experimental errors - based on the decomposition of the data into mutually incoherent modes - are proposed and numerically tested. We believe that the results presented here open up novel algorithmic methods to accelerate detector readout rates and enable subpixel resolution in propagation-based phase retrieval. Further the techniques are straightforward to be extended to methods such as CDI, ptychography and holography.

Core reactivity estimation in space reactors using recurrent dynamic networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Tsai, Wei K.

1991-01-01

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

[Portable Epileptic Seizure Monitoring Intelligent System Based on Android System].

PubMed

Liang, Zhenhu; Wu, Shufeng; Yang, Chunlin; Jiang, Zhenzhou; Yu, Tao; Lu, Chengbiao; Li, Xiaoli

2016-02-01

The clinical electroencephalogram (EEG) monitoring systems based on personal computer system can not meet the requirements of portability and home usage. The epilepsy patients have to be monitored in hospital for an extended period of time, which imposes a heavy burden on hospitals. In the present study, we designed a portable 16-lead networked monitoring system based on the Android smart phone. The system uses some technologies including the active electrode, the WiFi wireless transmission, the multi-scale permutation entropy (MPE) algorithm, the back-propagation (BP) neural network algorithm, etc. Moreover, the software of Android mobile application can realize the processing and analysis of EEG data, the display of EEG waveform and the alarm of epileptic seizure. The system has been tested on the mobile phones with Android 2. 3 operating system or higher version and the results showed that this software ran accurately and steadily in the detection of epileptic seizure. In conclusion, this paper provides a portable and reliable solution for epileptic seizure monitoring in clinical and home applications.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

MST's Programmable Power Supplies: Bt Update, Bp Prototype

NASA Astrophysics Data System (ADS)

Holly, D. J.; Chapman, B. E.; McCollam, K. J.; Morin, J. C.; Thomas, M. A.

2013-10-01

MST's toroidal field programmable power supply (Bt PPS) has now been in operation for several years and has provided important new capabilities. One of the primary goals for the Bt PPS is the partial optimization of inductive current profile control, involving control of the poloidal electric field. The Bt PPS has achieved fluctuation reduction over MST's entire range of Ip. At the largest Ip, the Bt PPS achieves fluctuation reduction with a smaller poloidal electric field than the previous passive system, implying that substantially longer periods of current profile control may be possible. The Bt PPS has also been used to produce Ohmic tokamak plasmas in MST. With an applied toroidal field of 0.135 T, and q(a) > 2, the estimated energy confinement time is roughly consistent with neo-Alcator scaling. Driving q(a) < 2 with larger Ip, the confinement time degrades, but the discharge duration does not terminate prematurely. To fully optimize current profile control and to test MST operational limits, a PPS is also needed for the Bp circuit. Currently in prototype stage, the Bp PPS will feature a number of innovations to increase its flexibility and performance. Isolated charging, control, and monitor systems will eliminate charging relays, reduce coupling between modules, and minimize capacitor heating. Seven-level pulse width modulation will reduce output ripple and switching losses. Solid state shorting bars will eliminate shorting relays and minimize wiring. A balanced switching algorithm will minimize capacitive noise generation. Work supported by U. S. D. o. E.

Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

PubMed Central

Rene, Eldon R.; López, M. Estefanía; Kim, Jung Hoon; Park, Hung Suck

2013-01-01

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3 ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3 ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R 2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently. PMID:24307999

A light intensity monitoring method based on fiber Bragg grating sensing technology and BP neural network

NASA Astrophysics Data System (ADS)

Li, Lu-Ming; Zhu, Qian; Zhang, Zhi-Guo; Cai, Zhi-Min; Liao, Zhi-Jun; Hu, Zhen-Yan

2017-04-01

In this paper, a light intensity monitoring method based on FBG is proposed. The method establishes a light intensity monitoring model with cantilever beam structure and BP neural network algorithm, which is based on fiber grating sensing technology. The accuracy of the model can meet the requirements of engineering project and it can monitor light intensity in real time. The experimental results show that the method has good stability and high sensitivity.

Selection and collection of multi parameter physiological data for cardiac rhythm diagnostic algorithm development

NASA Astrophysics Data System (ADS)

Bostock, J.; Weller, P.; Cooklin, M.

2010-07-01

Automated diagnostic algorithms are used in implantable cardioverter-defibrillators (ICD's) to detect abnormal heart rhythms. Algorithms misdiagnose and improved specificity is needed to prevent inappropriate therapy. Knowledge engineering (KE) and artificial intelligence (AI) could improve this. A pilot study of KE was performed with artificial neural network (ANN) as AI system. A case note review analysed arrhythmic events stored in patients ICD memory. 13.2% patients received inappropriate therapy. The best ICD algorithm had sensitivity 1.00, specificity 0.69 (p<0.001 different to gold standard). A subset of data was used to train and test an ANN. A feed-forward, back-propagation network with 7 inputs, a 4 node hidden layer and 1 output had sensitivity 1.00, specificity 0.71 (p<0.001). A prospective study was performed using KE to list arrhythmias, factors and indicators for which measurable parameters were evaluated and results reviewed by a domain expert. Waveforms from electrodes in the heart and thoracic bio-impedance; temperature and motion data were collected from 65 patients during cardiac electrophysiological studies. 5 incomplete datasets were due to technical failures. We concluded that KE successfully guided selection of parameters and ANN produced a usable system and that complex data collection carries greater risk of technical failure, leading to data loss.

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

PubMed

Olawoyin, Richard

2016-10-01

The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Appraisal of artificial neural network for forecasting of economic parameters

NASA Astrophysics Data System (ADS)

Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

2017-01-01

The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

A link prediction method for heterogeneous networks based on BP neural network

NASA Astrophysics Data System (ADS)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

PANDA: Protein function prediction using domain architecture and affinity propagation.

PubMed

Wang, Zheng; Zhao, Chenguang; Wang, Yiheng; Sun, Zheng; Wang, Nan

2018-02-22

We developed PANDA (Propagation of Affinity and Domain Architecture) to predict protein functions in the format of Gene Ontology (GO) terms. PANDA at first executes profile-profile alignment algorithm to search against PfamA, KOG, COG, and SwissProt databases, and then launches PSI-BLAST against UniProt for homologue search. PANDA integrates a domain architecture inference algorithm based on the Bayesian statistics that calculates the probability of having a GO term. All the candidate GO terms are pooled and filtered based on Z-score. After that, the remaining GO terms are clustered using an affinity propagation algorithm based on the GO directed acyclic graph, followed by a second round of filtering on the clusters of GO terms. We benchmarked the performance of all the baseline predictors PANDA integrates and also for every pooling and filtering step of PANDA. It can be found that PANDA achieves better performances in terms of area under the curve for precision and recall compared to the baseline predictors. PANDA can be accessed from http://dna.cs.miami.edu/PANDA/ .

Black Phosphorus (BP) Nanodots for Potential Biomedical Applications.

PubMed

Lee, Hyun Uk; Park, So Young; Lee, Soon Chang; Choi, Saehae; Seo, Soonjoo; Kim, Hyeran; Won, Jonghan; Choi, Kyuseok; Kang, Kyoung Suk; Park, Hyun Gyu; Kim, Hee-Sik; An, Ha Rim; Jeong, Kwang-Hun; Lee, Young-Chul; Lee, Jouhahn

2016-01-13

Recently, the appeal of 2D black phosphorus (BP) has been rising due to its unique optical and electronic properties with a tunable band gap (≈0.3-1.5 eV). While numerous research efforts have recently been devoted to nano- and optoelectronic applications of BP, no attention has been paid to promising medical applications. In this article, the preparation of BP-nanodots of a few nm to <20 nm with an average diameter of ≈10 nm and height of ≈8.7 nm is reported by a modified ultrasonication-assisted solution method. Stable formation of nontoxic phosphates and phosphonates from BP crystals with exposure in water or air is observed. As for the BP-nanodot crystals' stability (ionization and persistence of fluorescent intensity) in aqueous solution, after 10 d, ≈80% at 1.5 mg mL(-1) are degraded (i.e., ionized) in phosphate buffered saline. They showed no or little cytotoxic cell-viability effects in vitro involving blue- and green-fluorescence cell imaging. Thus, BP-nanodots can be considered a promising agent for drug delivery or cellular tracking systems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Classification of neocortical interneurons using affinity propagation.

PubMed

Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

2013-01-01

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

Assembly of human C-terminal binding protein (CtBP) into tetramers.

PubMed

Bellesis, Andrew G; Jecrois, Anne M; Hayes, Janelle A; Schiffer, Celia A; Royer, William E

2018-06-08

C-terminal binding protein 1 (CtBP1) and CtBP2 are transcriptional coregulators that repress numerous cellular processes, such as apoptosis, by binding transcription factors and recruiting chromatin-remodeling enzymes to gene promoters. The NAD(H)-linked oligomerization of human CtBP is coupled to its co-transcriptional activity, which is implicated in cancer progression. However, the biologically relevant level of CtBP assembly has not been firmly established; nor has the stereochemical arrangement of the subunits above that of a dimer. Here, multi-angle light scattering (MALS) data established the NAD + - and NADH-dependent assembly of CtBP1 and CtBP2 into tetramers. An examination of subunit interactions within CtBP1 and CtBP2 crystal lattices revealed that both share a very similar tetrameric arrangement resulting from assembly of two dimeric pairs, with specific interactions probably being sensitive to NAD(H) binding. Creating a series of mutants of both CtBP1 and CtBP2, we tested the hypothesis that the crystallographically observed interdimer pairing stabilizes the solution tetramer. MALS data confirmed that these mutants disrupt both CtBP1 and CtBP2 tetramers, with the dimer generally remaining intact, providing the first stereochemical models for tetrameric assemblies of CtBP1 and CtBP2. The crystal structure of a subtle destabilizing mutant suggested that small structural perturbations of the hinge region linking the substrate- and NAD-binding domains are sufficient to weaken the CtBP1 tetramer. These results strongly suggest that the tetramer is important in CtBP function, and the series of CtBP mutants reported here can be used to investigate the physiological role of the tetramer. © 2018 Bellesis et al.

Risk factors for low back pain and sciatica in elderly men-the MrOS Sweden study.

PubMed

Kherad, Mehrsa; Rosengren, Björn E; Hasserius, Ralph; Nilsson, Jan-Åke; Redlund-Johnell, Inga; Ohlsson, Claes; Mellström, Dan; Lorentzon, Mattiaz; Ljunggren, Östen; Karlsson, Magnus K

2017-01-08

The aim of this study was to identify whether factors beyond anatomical abnormalities are associated with low back pain (LBP) and LBP with sciatica (SCI) in older men. Mister Osteoporosis Sweden includes 3,014 men aged 69–81 years. They answered questionnaires on lifestyle and whether they had experienced LBP and SCI during the preceding 12 months. About 3,007 men answered the back pain (BP) questions, 258 reported BP without specified region. We identified 1,388 with no BP, 1,361 with any LBP (regardless of SCI), 1,074 of those with LBP also indicated if they had experienced LBP (n = 615), LBP+SCI (n = 459). About 49% of those with LBP and 54% of those with LBP+SCI rated their health as poor/very poor (P < 0.001). Men with any LBP to a greater extent than those without BP had poor self-estimated health, depressive symptoms, dizziness, fall tendency, serious comorbidity (diabetes, stroke, coronary heart disease, pulmonary disease and/or cancer) (all P < 0.001), foreign background, were smokers (all P < 0.01), had low physical activity and used walking aids (all P < 0.05). Men with LBP+SCI to a greater extent than those with LBP had lower education, lower self-estimated health, comorbidity, dizziness and used walking aids (all P < 0.001). In older men with LBP and SCI, anatomical abnormalities such as vertebral fractures, metastases, central or lateral spinal stenosis or degenerative conditions may only in part explain prevalent symptoms and disability. Social and lifestyle factors must also be evaluated since they are associated not only with unspecific LBP but also with LBP with SCI.

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm*

PubMed Central

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-01-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement. PMID:20617122

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

PubMed

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-02-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.

Architecture of the 99 bp DNA-six-protein regulatory complex of the lambda att site.

PubMed

Sun, Xingmin; Mierke, Dale F; Biswas, Tapan; Lee, Sang Yeol; Landy, Arthur; Radman-Livaja, Marta

2006-11-17

The highly directional and tightly regulated recombination reaction used to site-specifically excise the bacteriophage lambda chromosome out of its E. coli host chromosome requires the binding of six sequence-specific proteins to a 99 bp segment of the phage att site. To gain structural insights into this recombination pathway, we measured 27 FRET distances between eight points on the 99 bp regulatory DNA bound with all six proteins. Triangulation of these distances using a metric matrix distance-geometry algorithm provided coordinates for these eight points. The resulting path for the protein-bound regulatory DNA, which fits well with the genetics, biochemistry, and X-ray crystal structures describing the individual proteins and their interactions with DNA, provides a new structural perspective into the molecular mechanism and regulation of the recombination reaction and illustrates a design by which different families of higher-order complexes can be assembled from different numbers and combinations of the same few proteins.

An Accurate Fire-Spread Algorithm in the Weather Research and Forecasting Model Using the Level-Set Method

NASA Astrophysics Data System (ADS)

Muñoz-Esparza, Domingo; Kosović, Branko; Jiménez, Pedro A.; Coen, Janice L.

2018-04-01

The level-set method is typically used to track and propagate the fire perimeter in wildland fire models. Herein, a high-order level-set method using fifth-order WENO scheme for the discretization of spatial derivatives and third-order explicit Runge-Kutta temporal integration is implemented within the Weather Research and Forecasting model wildland fire physics package, WRF-Fire. The algorithm includes solution of an additional partial differential equation for level-set reinitialization. The accuracy of the fire-front shape and rate of spread in uncoupled simulations is systematically analyzed. It is demonstrated that the common implementation used by level-set-based wildfire models yields to rate-of-spread errors in the range 10-35% for typical grid sizes (Δ = 12.5-100 m) and considerably underestimates fire area. Moreover, the amplitude of fire-front gradients in the presence of explicitly resolved turbulence features is systematically underestimated. In contrast, the new WRF-Fire algorithm results in rate-of-spread errors that are lower than 1% and that become nearly grid independent. Also, the underestimation of fire area at the sharp transition between the fire front and the lateral flanks is found to be reduced by a factor of ≈7. A hybrid-order level-set method with locally reduced artificial viscosity is proposed, which substantially alleviates the computational cost associated with high-order discretizations while preserving accuracy. Simulations of the Last Chance wildfire demonstrate additional benefits of high-order accurate level-set algorithms when dealing with complex fuel heterogeneities, enabling propagation across narrow fuel gaps and more accurate fire backing over the lee side of no fuel clusters.

Symbolic Algebra Development for Higher-Order Electron Propagator Formulation and Implementation.

PubMed

Tamayo-Mendoza, Teresa; Flores-Moreno, Roberto

2014-06-10

Through the use of symbolic algebra, implemented in a program, the algebraic expression of the elements of the self-energy matrix for the electron propagator to different orders were obtained. In addition, a module for the software package Lowdin was automatically generated. Second- and third-order electron propagator results have been calculated to test the correct operation of the program. It was found that the Fortran 90 modules obtained automatically with our algorithm succeeded in calculating ionization energies with the second- and third-order electron propagator in the diagonal approximation. The strategy for the development of this symbolic algebra program is described in detail. This represents a solid starting point for the automatic derivation and implementation of higher-order electron propagator methods.

Around and beyond 53BP1 Nuclear Bodies.

PubMed

Fernandez-Vidal, Anne; Vignard, Julien; Mirey, Gladys

2017-12-05

Within the nucleus, sub-nuclear domains define territories where specific functions occur. Nuclear bodies (NBs) are dynamic structures that concentrate nuclear factors and that can be observed microscopically. Recently, NBs containing the p53 binding protein 1 (53BP1), a key component of the DNA damage response, were defined. Interestingly, 53BP1 NBs are visualized during G1 phase, in daughter cells, while DNA damage was generated in mother cells and not properly processed. Unlike most NBs involved in transcriptional processes, replication has proven to be key for 53BP1 NBs, with replication stress leading to the formation of these large chromatin domains in daughter cells. In this review, we expose the composition and organization of 53BP1 NBs and focus on recent findings regarding their regulation and dynamics. We then concentrate on the importance of the replication stress, examine the relation of 53BP1 NBs with DNA damage and discuss their dysfunction.

Around and beyond 53BP1 Nuclear Bodies

PubMed Central

Fernandez-Vidal, Anne; Vignard, Julien

2017-01-01

Within the nucleus, sub-nuclear domains define territories where specific functions occur. Nuclear bodies (NBs) are dynamic structures that concentrate nuclear factors and that can be observed microscopically. Recently, NBs containing the p53 binding protein 1 (53BP1), a key component of the DNA damage response, were defined. Interestingly, 53BP1 NBs are visualized during G1 phase, in daughter cells, while DNA damage was generated in mother cells and not properly processed. Unlike most NBs involved in transcriptional processes, replication has proven to be key for 53BP1 NBs, with replication stress leading to the formation of these large chromatin domains in daughter cells. In this review, we expose the composition and organization of 53BP1 NBs and focus on recent findings regarding their regulation and dynamics. We then concentrate on the importance of the replication stress, examine the relation of 53BP1 NBs with DNA damage and discuss their dysfunction. PMID:29206178

Radiowave propagation measurements in Nigeria (preliminary reports)

NASA Astrophysics Data System (ADS)

Falodun, S. E.; Okeke, P. N.

2013-07-01

International conferences on frequency coordination have, in recent years, required new information on radiowave propagation in tropical regions and, in particular, on propagation in Africa. The International Telecommunications Union (ITU-R) initiated `radio-wave propagation measurement campaign' in some African countries some years back. However, none of the ITU-initiated experiments were mounted in Nigeria, and hence, there is lack of adequate understanding of the propagation mechanisms associated with this region of the tropics. The Centre for Basic Space Science (CBSS) of NASRDA has therefore embarked on propagation data collection from the different climatic zones of Nigeria (namely Coastal, Guinea Savannah, Midland, and Sahelian) with the aim of making propagation data available to the ITU, for design and prediction purposes in order to ensure a qualitative and effective communication system in Nigeria. This paper focuses on the current status of propagation data from Nigeria (collected by CBSS), identifying other parameters that still need to be obtained. The centre has deployed weather stations to different locations in the country for refractivity measurements in clear atmosphere, at the ground surface and at an altitude of 100 m, being the average height of communication mast in Nigeria. Other equipments deployed are Micro Rain Radar and Nigerian Environmental and Climatic Observing Program equipments. Some of the locations of the measurement stations are Nsukka (7.4° E, 6.9° N), Akure (5.12° E, 7.15° N), Minna (6.5° E, 9.6° N), Sokoto (5.25° E, 13.08° N), Jos (8.9° E, 9.86° N), and Lagos (3.35° E, 6.6° N). The results obtained from the data analysis have shown that the refractivity values vary with climatic zones and seasons of the year. Also, the occurrence probability of abnormal propagation events, such as super refraction, sub-refraction, and ducting, depends on the location as well as the local time. We have also attempted to identify

Effectiveness and cost-effectiveness of a guided Internet- and mobile-based intervention for the indicated prevention of major depression in patients with chronic back pain-study protocol of the PROD-BP multicenter pragmatic RCT.

PubMed

Sander, L; Paganini, S; Lin, J; Schlicker, S; Ebert, D D; Buntrock, C; Baumeister, H

2017-01-21

Reducing the disease burden of major depressive disorder (MDD) is of major public health relevance. The prevention of depression is regarded as one possible approach to reach this goal. People with multiple risk factors for MDD such as chronic back pain and subthreshold depressive symptoms may benefit most from preventive measures. The Internet as intervention setting allows for scaling up preventive interventions on a public mental health level. This study is a multicenter pragmatic randomized controlled trial (RCT) of parallel design aiming to investigate the (cost-) effectiveness of an Internet- and mobile-based intervention (IMI) for the prevention of depression in chronic back pain patients (PROD-BP) with subthreshold depressive symptoms. eSano BackCare-DP is a guided, chronic back pain-specific depression prevention intervention based on cognitive behavioral therapy (CBT) principles comprising six weekly plus three optional modules and two booster sessions after completion of the intervention. Trained psychologists provide guidance by sending feedback messages after each module. A total of 406 patients with chronic back pain and without a depressive disorder at baseline will be recruited following orthopedic rehabilitation care and allocated to either intervention or treatment-as-usual (TAU). Primary patient-relevant endpoint of the trial is the time to onset of MDD measured by the telephone-administered Structured Clinical Interview for DSM (SCID) at baseline and 1-year post-randomization. Key secondary outcomes are health-related quality of life, depression severity, pain intensity, pain-related disability, ability to work, intervention satisfaction and adherence as well as side effects of the intervention. Online assessments take place at baseline and 9 weeks as well as 6 and 12 months post-randomization. Cox regression survival analysis will be conducted to estimate hazard ratio at 12-month follow-up. Moreover, an economic analysis will be conducted

Influence of Artisan Bakery- or Laboratory-Propagated Sourdoughs on the Diversity of Lactic Acid Bacterium and Yeast Microbiotas

PubMed Central

Minervini, Fabio; Lattanzi, Anna; De Angelis, Maria; Gobbetti, Marco

2012-01-01

Seven mature type I sourdoughs were comparatively back-slopped (80 days) at artisan bakery and laboratory levels under constant technology parameters. The cell density of presumptive lactic acid bacteria and related biochemical features were not affected by the environment of propagation. On the contrary, the number of yeasts markedly decreased from artisan bakery to laboratory propagation. During late laboratory propagation, denaturing gradient gel electrophoresis (DGGE) showed that the DNA band corresponding to Saccharomyces cerevisiae was no longer detectable in several sourdoughs. Twelve species of lactic acid bacteria were variously identified through a culture-dependent approach. All sourdoughs harbored a certain number of species and strains, which were dominant throughout time and, in several cases, varied depending on the environment of propagation. As shown by statistical permutation analysis, the lactic acid bacterium populations differed among sourdoughs propagated at artisan bakery and laboratory levels. Lactobacillus plantarum, Lactobacillus sakei, and Weissella cibaria dominated in only some sourdoughs back-slopped at artisan bakeries, and Leuconostoc citreum seemed to be more persistent under laboratory conditions. Strains of Lactobacillus sanfranciscensis were indifferently found in some sourdoughs. Together with the other stable species and strains, other lactic acid bacteria temporarily contaminated the sourdoughs and largely differed between artisan bakery and laboratory levels. The environment of propagation has an undoubted influence on the composition of sourdough yeast and lactic acid bacterium microbiotas. PMID:22635989

TU-AB-BRA-12: Impact of Image Registration Algorithms On the Prediction of Pathological Response with Radiomic Textures

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

Yip, S; Coroller, T; Niu, N

2015-06-15

Purpose: Tumor regions-of-interest (ROI) can be propagated from the pre-onto the post-treatment PET/CT images using image registration of their CT counterparts, providing an automatic way to compute texture features on longitudinal scans. This exploratory study assessed the impact of image registration algorithms on textures to predict pathological response. Methods: Forty-six esophageal cancer patients (1 tumor/patient) underwent PET/CT scans before and after chemoradiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumor ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. One co-occurrence, two run-length and size zone matrix texturesmore » were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs and texture quantification resulting from different algorithms were compared using overlap volume (OV) and coefficient of variation (CoV), respectively. Results: Tumor volumes were better captured by ROIs propagated by deformable rather than the rigid registration. The OV between rigidly and deformably propagated ROIs were 69%. The deformably propagated ROIs were found to be similar (OV∼80%) except for fast-demons (OV∼60%). Rigidly propagated ROIs with run-length matrix textures failed to significantly differentiate between responders and non-responders (AUC=0.65, p=0.07), while the differentiation was significant with other textures (AUC=0.69–0.72, p<0.03). Among the deformable algorithms, fast-demons was the least predictive (AUC=0.68–0.71, p<0.04). ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC=0.71–0.78, p<0.01) despite substantial

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

AWARE - The Automated EUV Wave Analysis and REduction algorithm

NASA Astrophysics Data System (ADS)

Ireland, J.; Inglis; A. R.; Shih, A. Y.; Christe, S.; Mumford, S.; Hayes, L. A.; Thompson, B. J.

2016-10-01

Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. Since their discovery over two hundred papers discussing their properties, causes and physics have been published. However, their fundamental nature and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, and their relation to other solar phenomena, we have constructed the Automated Wave Analysis and REduction (AWARE) algorithm for the detection of EUV waves over the full Sun. The AWARE algorithm is based on a novel image processing approach to isolating the bright wavefront of the EUV as it propagates across the corona. AWARE detects the presence of a wavefront, and measures the distance, velocity and acceleration of that wavefront across the Sun. Results from AWARE are compared to results from other algorithms for some well known EUV wave events. Suggestions are also give for further refinements to the basic algorithm presented here.

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

Comparison of Microlife BP A200 Plus and Omron M6 blood pressure monitors to detect atrial fibrillation in hypertensive patients.

PubMed

Marazzi, Giuseppe; Iellamo, Ferdinando; Volterrani, Maurizio; Lombardo, Mauro; Pelliccia, Francesco; Righi, Daniela; Grieco, Fabrizia; Cacciotti, Luca; Iaia, Luigi; Caminiti, Giuseppe; Rosano, Giuseppe

2012-01-01

Self-monitoring home blood pressure (BP) devices are currently recommended for long-term follow-up of hypertension and its management. Some of these devices are integrated with algorithms aimed at detecting atrial fibrillation (AF), which is common essential hypertension. This study was designed to compare the diagnostic accuracy of two widely diffused home BP monitoring devices in detecting AF in an unselected population of outpatients referred to a hypertension clinic because of high BP. In 503 consecutive patients the authors simultaneously compared the accuracy of the Microlife(®) BP A200 Plus (Microlife) and the OMRON(®) M6 (OMRON) home BP devices, in detecting AF. Systolic and diastolic BP as well as heart rate (HR) values detected by the two devices were not significantly different. Pulse irregularity was detected in 124 and 112 patients with the OMRON M6 and Microlife BP A200 Plus devices, respectively. Simultaneous electrocardiogram (ECG) recording revealed that pulse irregularity was due to AF in 101 patients. Pulse irregularity detected by the OMRON M6 device corresponded to AF in 101, to supraventricular premature beats in 18, and to frequent premature ventricular beat in five patients, respectively. Pulse irregularity detected by the Microlife BP A200 Plus device corresponded to AF in 93, to supraventricular premature beats in 14, and to ventricular premature beats in five patients. The sensitivity for detecting AF was 100%, the specificity was 92%, and diagnostic accuracy 95% for the OMRON M6 and 100%, 92%, and 95 for the Microlife BP A200 Plus, respectively. AF was newly diagnosed by ECG recordings in 47 patients, and was detected in all patients by the OMRON device, and in 42 patients by the Microlife device. These results indicate that OMRON M6 is more accurate than Microlife BP A200 Plus in detecting AF in patients with essential hypertension. Widespread use of these devices in hypertensive patients could be of clinical benefit for the early

Analytical modeling of flash-back phenomena. [premixed/prevaporized combustion system

NASA Technical Reports Server (NTRS)

Feng, C. C.

1979-01-01

To understand the flame flash-back phenomena more extensively, an analytical model was formed and a numerical program was written and tested to solve the set of differential equations describing the model. Results show that under a given set of conditions flame propagates in the boundary layer on a flat plate when the free stream is at or below 1.8 m/s.

Assessing performance of flaw characterization methods through uncertainty propagation

NASA Astrophysics Data System (ADS)

Miorelli, R.; Le Bourdais, F.; Artusi, X.

2018-04-01

In this work, we assess the inversion performance in terms of crack characterization and localization based on synthetic signals associated to ultrasonic and eddy current physics. More precisely, two different standard iterative inversion algorithms are used to minimize the discrepancy between measurements (i.e., the tested data) and simulations. Furthermore, in order to speed up the computational time and get rid of the computational burden often associated to iterative inversion algorithms, we replace the standard forward solver by a suitable metamodel fit on a database built offline. In a second step, we assess the inversion performance by adding uncertainties on a subset of the database parameters and then, through the metamodel, we propagate these uncertainties within the inversion procedure. The fast propagation of uncertainties enables efficiently evaluating the impact due to the lack of knowledge on some parameters employed to describe the inspection scenarios, which is a situation commonly encountered in the industrial NDE context.

A space-time discretization procedure for wave propagation problems

NASA Technical Reports Server (NTRS)

Davis, Sanford

1989-01-01

Higher order compact algorithms are developed for the numerical simulation of wave propagation by using the concept of a discrete dispersion relation. The dispersion relation is the imprint of any linear operator in space-time. The discrete dispersion relation is derived from the continuous dispersion relation by examining the process by which locally plane waves propagate through a chosen grid. The exponential structure of the discrete dispersion relation suggests an efficient splitting of convective and diffusive terms for dissipative waves. Fourth- and eighth-order convection schemes are examined that involve only three or five spatial grid points. These algorithms are subject to the same restrictions that govern the use of dispersion relations in the constructions of asymptotic expansions to nonlinear evolution equations. A new eighth-order scheme is developed that is exact for Courant numbers of 1, 2, 3, and 4. Examples are given of a pulse and step wave with a small amount of physical diffusion.

Benzo[a]pyrene (BP) DNA adduct formation in DNA repair–deficient p53 haploinsufficient [Xpa(−/−)p53(+/−)] and wild-type mice fed BP and BP plus chlorophyllin for 28 days

PubMed Central

Poirier, Miriam C.

2012-01-01

We have evaluated DNA damage (DNA adduct formation) after feeding benzo[a]pyrene (BP) to wild-type (WT) and cancer-susceptible Xpa(−/−)p53(+/−) mice deficient in nucleotide excision repair and haploinsufficient for the tumor suppressor p53. DNA damage was evaluated by high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ES-MS/MS), which measures r7,t8,t9-trihydroxy-c-10-(N 2-deoxyguanosyl)-7,8,9,10-tetrahydrobenzo[a]pyrene (BPdG), and a chemiluminescence immunoassay (CIA), using anti-r7,t8-dihydroxy-t-9,10-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BPDE)–DNA antiserum, which measures both BPdG and the other stable BP-DNA adducts. When mice were fed 100 ppm BP for 28 days, BP-induced DNA damage measured in esophagus, liver and lung was typically higher in Xpa(−/−)p53(+/−) mice, compared with WT mice. This result is consistent with the previously observed tumor susceptibility of Xpa(−/−)p53(+/−) mice. BPdG, the major DNA adduct associated with tumorigenicity, was the primary DNA adduct formed in esophagus (a target tissue in the mouse), whereas total BP-DNA adducts predominated in higher levels in the liver (a non-target tissue in the mouse). In an attempt to lower BP-induced DNA damage, we fed the WT and Xpa(−/−)p53(+/−) mice 0.3% chlorophyllin (CHL) in the BP-containing diet for 28 days. The addition of CHL resulted in an increase of BP–DNA adducts in esophagus, liver and lung of WT mice, a lowering of BPdG in esophagi of WT mice and livers of Xpa(−/−)p53(+/−) mice and an increase of BPdG in livers of WT mice. Therefore, the addition of CHL to a BP-containing diet showed a lack of consistent chemoprotective effect, indicating that oral CHL administration may not reduce PAH–DNA adduct levels consistently in human organs. PMID:22828138

RanBP9 at the intersection between cofilin and Aβ pathologies: rescue of neurodegenerative changes by RanBP9 reduction.

PubMed

Woo, J A; Boggess, T; Uhlar, C; Wang, X; Khan, H; Cappos, G; Joly-Amado, A; De Narvaez, E; Majid, S; Minamide, L S; Bamburg, J R; Morgan, D; Weeber, E; Kang, D E

2015-03-05

Molecular pathways underlying the neurotoxicity and production of amyloid β protein (Aβ) represent potentially promising therapeutic targets for Alzheimer's disease (AD). We recently found that overexpression of the scaffolding protein RanBP9 increases Aβ production in cell lines and in transgenic mice while promoting cofilin activation and mitochondrial dysfunction. Translocation of cofilin to mitochondria and induction of cofilin-actin pathology require the activation/dephosphorylation of cofilin by Slingshot homolog 1 (SSH1) and cysteine oxidation of cofilin. In this study, we found that endogenous RanBP9 positively regulates SSH1 levels and mediates Aβ-induced translocation of cofilin to mitochondria and induction of cofilin-actin pathology in cultured cells, primary neurons, and in vivo. Endogenous level of RanBP9 was also required for Aβ-induced collapse of growth cones in immature neurons (days in vitro 9 (DIV9)) and depletion of synaptic proteins in mature neurons (DIV21). In vivo, amyloid precursor protein (APP)/presenilin-1 (PS1) mice exhibited 3.5-fold increased RanBP9 levels, and RanBP9 reduction protected against cofilin-actin pathology, synaptic damage, gliosis, and Aβ accumulation associated with APP/PS1 mice. Brains slices derived from APP/PS1 mice showed significantly impaired long-term potentiation (LTP), and RanBP9 reduction significantly enhanced paired pulse facilitation and LTP, as well as partially rescued contextual memory deficits associated with APP/PS1 mice. Therefore, these results underscore the critical importance of endogenous RanBP9 not only in Aβ accumulation but also in mediating the neurotoxic actions of Aβ at the level of synaptic plasticity, mitochondria, and cofilin-actin pathology via control of the SSH1-cofilin pathway in vivo.

Air Monitoring Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Water Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Waste Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Air Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Sediment Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines

PubMed Central

Xiao, Qiyang; Li, Jian; Bai, Zhiliang; Sun, Jiedi; Zhou, Nan; Zeng, Zhoumo

2016-01-01

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods. PMID:27983577

Combined wave propagation analysis of earthquake recordings from borehole and building sensors

NASA Astrophysics Data System (ADS)

Petrovic, B.; Parolai, S.; Dikmen, U.; Safak, E.; Moldobekov, B.; Orunbaev, S.

2015-12-01

In regions highly exposed to natural hazards, Early Warning Systems can play a central role in risk management and mitigation procedures. To improve at a relatively low cost the spatial resolution of regional earthquake early warning (EEW) systems, decentralized onsite EEW and building monitoring, a wireless sensing unit, the Self-Organizing Seismic Early Warning Information Network (SOSEWIN) was developed and further improved to include the multi-parameter acquisition. SOSEWINs working in continuous real time mode are currently tested on various sites. In Bishkek and Istanbul, an instrumented building is located close to a borehole equipped with downhole sensors. The joint data analysis of building and borehole earthquake recordings allows the study of the behavior of the building, characteristics of the soil, and soil-structure interactions. The interferometric approach applied to recordings of the building response is particularly suitable to characterize the wave propagation inside a building, including the propagation velocity of shear waves and attenuation. Applied to borehole sensors, it gives insights into velocity changes in different layers, reflections and mode conversion, and allows the estimation of the quality factor Qs. We used combined building and borehole data from the two test sites: 1) to estimate the characteristics of wave propagation through the building to the soil and back, and 2) to obtain an empirical insight into soil-structure interactions. The two test sites represent two different building and soil types, and soil structure impedance contrasts. The wave propagation through the soil to the building and back is investigated by the joint interferometric approach. The propagation of up and down-going waves through the building and soil is clearly imaged and the reflection of P and S waves from the earth surface and the top of the building identified. An estimate of the reflected and transmitted energy amounts is given, too.

Testing of Gyroless Estimation Algorithms for the Fuse Spacecraft

NASA Technical Reports Server (NTRS)

Harman, R.; Thienel, J.; Oshman, Yaakov

2004-01-01

This paper documents the testing and development of magnetometer-based gyroless attitude and rate estimation algorithms for the Far Ultraviolet Spectroscopic Explorer (FUSE). The results of two approaches are presented, one relies on a kinematic model for propagation, a method used in aircraft tracking, and the other is a pseudolinear Kalman filter that utilizes Euler's equations in the propagation of the estimated rate. Both algorithms are tested using flight data collected over a few months after the failure of two of the reaction wheels. The question of closed-loop stability is addressed. The ability of the controller to meet the science slew requirements, without the gyros, is analyzed.

Variable Penetrance of the 15q11.2 BP1-BP2 Microduplication in a Family with Cognitive and Language Impairment

PubMed Central

Benítez-Burraco, Antonio; Barcos-Martínez, Montserrat; Espejo-Portero, Isabel; Jiménez-Romero, Salud

2017-01-01

The 15q11.2 BP1-BP2 region is found duplicated or deleted in people with cognitive, language, and behavioral impairment. We report on a family (a father and 3 male twin siblings) that presents with a duplication of the 15q11.2 BP1-BP2 region and a variable phenotype: the father and the fraternal twin are normal carriers, whereas the monozygotic twins exhibit severe language and cognitive delay as well as behavioral disturbances. The genes located within the duplicated region are involved in brain development and function, and some of them are related to language processing. The probands' phenotype may result from changes in the expression level of some of these genes important for cognitive development. PMID:28588435

Injection and waveguiding properties in SU8 nanotubes for sub-wavelength regime propagation and nanophotonics integration

NASA Astrophysics Data System (ADS)

Bigeon, John; Huby, Nolwenn; Duvail, Jean-Luc; Bêche, Bruno

2014-04-01

We report photonic concepts related to injection and sub-wavelength propagation in nanotubes, an unusual but promising geometry for highly integrated photonic devices. Theoretical simulation by the finite domain time-dependent (FDTD) method was first used to determine the features of the direct light injection and sub-wavelength propagation regime within nanotubes. Then, the injection into nanotubes of SU8, a photoresist used for integrated photonics, was successfully achieved by using polymer microlensed fibers with a sub-micronic radius of curvature, as theoretically expected from FDTD simulations. The propagation losses in a single SU8 nanotube were determined by using a comprehensive set-up and a protocol for optical characterization. The attenuation coefficient has been evaluated at 1.25 dB mm-1 by a cut-back method transposed to such nanostructures. The mechanisms responsible for losses in nanotubes were identified with FDTD theoretical support. Both injection and cut-back methods developed here are compatible with any sub-micronic structures. This work on SU8 nanotubes suggests broader perspectives for future nanophotonics.

Injection and waveguiding properties in SU8 nanotubes for sub-wavelength regime propagation and nanophotonics integration.

PubMed

Bigeon, John; Huby, Nolwenn; Duvail, Jean-Luc; Bêche, Bruno

2014-05-21

We report photonic concepts related to injection and sub-wavelength propagation in nanotubes, an unusual but promising geometry for highly integrated photonic devices. Theoretical simulation by the finite domain time-dependent (FDTD) method was first used to determine the features of the direct light injection and sub-wavelength propagation regime within nanotubes. Then, the injection into nanotubes of SU8, a photoresist used for integrated photonics, was successfully achieved by using polymer microlensed fibers with a sub-micronic radius of curvature, as theoretically expected from FDTD simulations. The propagation losses in a single SU8 nanotube were determined by using a comprehensive set-up and a protocol for optical characterization. The attenuation coefficient has been evaluated at 1.25 dB mm(-1) by a cut-back method transposed to such nanostructures. The mechanisms responsible for losses in nanotubes were identified with FDTD theoretical support. Both injection and cut-back methods developed here are compatible with any sub-micronic structures. This work on SU8 nanotubes suggests broader perspectives for future nanophotonics.

Numerical study of signal propagation in corrugated coaxial cables

DOE PAGES

Li, Jichun; Machorro, Eric A.; Shields, Sidney

2017-01-01

Our article focuses on high-fidelity modeling of signal propagation in corrugated coaxial cables. Taking advantage of the axisymmetry, the authors reduce the 3-D problem to a 2-D problem by solving time-dependent Maxwell's equations in cylindrical coordinates.They then develop a nodal discontinuous Galerkin method for solving their model equations. We prove stability and error analysis for the semi-discrete scheme. We we present our numerical results, we demonstrate that our algorithm not only converges as our theoretical analysis predicts, but it is also very effective in solving a variety of signal propagation problems in practical corrugated coaxial cables.

Objective evaluation of linear and nonlinear tomosynthetic reconstruction algorithms

NASA Astrophysics Data System (ADS)

Webber, Richard L.; Hemler, Paul F.; Lavery, John E.

2000-04-01

This investigation objectively tests five different tomosynthetic reconstruction methods involving three different digital sensors, each used in a different radiologic application: chest, breast, and pelvis, respectively. The common task was to simulate a specific representative projection for each application by summation of appropriately shifted tomosynthetically generated slices produced by using the five algorithms. These algorithms were, respectively, (1) conventional back projection, (2) iteratively deconvoluted back projection, (3) a nonlinear algorithm similar to back projection, except that the minimum value from all of the component projections for each pixel is computed instead of the average value, (4) a similar algorithm wherein the maximum value was computed instead of the minimum value, and (5) the same type of algorithm except that the median value was computed. Using these five algorithms, we obtained data from each sensor-tissue combination, yielding three factorially distributed series of contiguous tomosynthetic slices. The respective slice stacks then were aligned orthogonally and averaged to yield an approximation of a single orthogonal projection radiograph of the complete (unsliced) tissue thickness. Resulting images were histogram equalized, and actual projection control images were subtracted from their tomosynthetically synthesized counterparts. Standard deviations of the resulting histograms were recorded as inverse figures of merit (FOMs). Visual rankings of image differences by five human observers of a subset (breast data only) also were performed to determine whether their subjective observations correlated with homologous FOMs. Nonparametric statistical analysis of these data demonstrated significant differences (P > 0.05) between reconstruction algorithms. The nonlinear minimization reconstruction method nearly always outperformed the other methods tested. Observer rankings were similar to those measured objectively.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN.

PubMed

Sen, Alper; Gümüsay, M Umit; Kavas, Aktül; Bulucu, Umut

2008-09-25

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

Evidence that Ribulose 1,5-Bisphosphate (RuBP) Binds to Inactive Sites of RuBP Carboxylase in Vivo and an Estimate of the Rate Constant for Dissociation 1

PubMed Central

Cardon, Zoe G.; Mott, Keith A.

1989-01-01

The binding of ribulose 1,5-bisphosphate (RuBP) to inactive (noncarbamylated) sites of the enzyme RuBP carboxylase in vivo was investigated in Spinacia oleracea and Helianthus annuus. The concentrations of RuBP and inactive sites were determined in leaf tissue as a function of time after a change to darkness. RuBP concentrations fell rapidly after the change to darkness and were approximately equal to the concentration of inactive sites after 60 s. Variations in the concentration of inactive sites, which were induced by differences in the light intensity before the light-dark transition, correlated with the concentration of RuBP between 60 and 120 s after the change to darkness. These data are discussed as evidence that RuBP binds to inactive sites of RuBP carboxylase in vivo. After the concentration of RuBP fell below that of inactive sites (at times longer than 60 s of darkness), the decline in RuBP was logarithmic with time. This would be expected if the dissociation of RuBP from inactive sites controlled the decline in RuBP concentration. These data were used to estimate the rate constant for dissociation of RuBP from inactive sites in vivo. PMID:16666692

Quantum and electromagnetic propagation with the conjugate symmetric Lanczos method.

PubMed

Acevedo, Ramiro; Lombardini, Richard; Turner, Matthew A; Kinsey, James L; Johnson, Bruce R

2008-02-14

The conjugate symmetric Lanczos (CSL) method is introduced for the solution of the time-dependent Schrodinger equation. This remarkably simple and efficient time-domain algorithm is a low-order polynomial expansion of the quantum propagator for time-independent Hamiltonians and derives from the time-reversal symmetry of the Schrodinger equation. The CSL algorithm gives forward solutions by simply complex conjugating backward polynomial expansion coefficients. Interestingly, the expansion coefficients are the same for each uniform time step, a fact that is only spoiled by basis incompleteness and finite precision. This is true for the Krylov basis and, with further investigation, is also found to be true for the Lanczos basis, important for efficient orthogonal projection-based algorithms. The CSL method errors roughly track those of the short iterative Lanczos method while requiring fewer matrix-vector products than the Chebyshev method. With the CSL method, only a few vectors need to be stored at a time, there is no need to estimate the Hamiltonian spectral range, and only matrix-vector and vector-vector products are required. Applications using localized wavelet bases are made to harmonic oscillator and anharmonic Morse oscillator systems as well as electrodynamic pulse propagation using the Hamiltonian form of Maxwell's equations. For gold with a Drude dielectric function, the latter is non-Hermitian, requiring consideration of corrections to the CSL algorithm.

Bayesian Image Segmentations by Potts Prior and Loopy Belief Propagation

NASA Astrophysics Data System (ADS)

Tanaka, Kazuyuki; Kataoka, Shun; Yasuda, Muneki; Waizumi, Yuji; Hsu, Chiou-Ting

2014-12-01

This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms are often referred to as hyperparameters in statistical machine learning theory. In order to determine these hyperparameters, we propose a new scheme for hyperparameter estimation based on conditional maximization of entropy in the Potts prior. The algorithm is given based on loopy belief propagation. In addition, we compare our conditional maximum entropy framework with the conventional maximum likelihood framework, and also clarify how the first order phase transitions in loopy belief propagations for Potts models influence our hyperparameter estimation procedures.

Numerical analysis of back pressure equal channel angular pressing of an Al-Mg alloy

NASA Astrophysics Data System (ADS)

Comăneci, R.

2017-08-01

Ultrafine grain size provides enhanced mechanical and/or physical properties such as strength and high ductility, superplasticity at relatively low temperatures and high strain rate and better corrosion resistance. Well-known as one of the most promising and effective structure refining method among other severe plastic deformation (SPD) techniques, equal channel angular pressing (ECAP) has been intensively investigated due to spectacular improvements in structure and therefore properties of bulk ultrafine grained/nanostructured materials. A successful ECAP requires surpassing two obstacles: the necessary load level which directly affects tools and a favourable stress distribution so the material withstanding the accumulated strain of repeated deformation. Materials could withstand more passes if a back pressure (BP) is applied. In traditional ECAP, tensile stress along the contact surface between the work piece and the upper wall of the outlet channel leads to crack initiation, while in the presence of BP, a negative (compressive) stress appears during the process balancing the tensile stress. In this study a comparative tridimensional finite element analysis (FEA) is performed to evaluate the flow of an Al-Mg alloy depending on different BP levels and process parameters. The results in terms of load level and strain distribution show the influence of BP on the material behaviour, opening opportunities for industrial applications.

Portable Wideband Microwave Imaging System for Intracranial Hemorrhage Detection Using Improved Back-projection Algorithm with Model of Effective Head Permittivity

PubMed Central

Mobashsher, Ahmed Toaha; Mahmoud, A.; Abbosh, A. M.

2016-01-01

Intracranial hemorrhage is a medical emergency that requires rapid detection and medication to restrict any brain damage to minimal. Here, an effective wideband microwave head imaging system for on-the-spot detection of intracranial hemorrhage is presented. The operation of the system relies on the dielectric contrast between healthy brain tissues and a hemorrhage that causes a strong microwave scattering. The system uses a compact sensing antenna, which has an ultra-wideband operation with directional radiation, and a portable, compact microwave transceiver for signal transmission and data acquisition. The collected data is processed to create a clear image of the brain using an improved back projection algorithm, which is based on a novel effective head permittivity model. The system is verified in realistic simulation and experimental environments using anatomically and electrically realistic human head phantoms. Quantitative and qualitative comparisons between the images from the proposed and existing algorithms demonstrate significant improvements in detection and localization accuracy. The radiation and thermal safety of the system are examined and verified. Initial human tests are conducted on healthy subjects with different head sizes. The reconstructed images are statistically analyzed and absence of false positive results indicate the efficacy of the proposed system in future preclinical trials. PMID:26842761

Portable Wideband Microwave Imaging System for Intracranial Hemorrhage Detection Using Improved Back-projection Algorithm with Model of Effective Head Permittivity

NASA Astrophysics Data System (ADS)

Mobashsher, Ahmed Toaha; Mahmoud, A.; Abbosh, A. M.

2016-02-01

Intracranial hemorrhage is a medical emergency that requires rapid detection and medication to restrict any brain damage to minimal. Here, an effective wideband microwave head imaging system for on-the-spot detection of intracranial hemorrhage is presented. The operation of the system relies on the dielectric contrast between healthy brain tissues and a hemorrhage that causes a strong microwave scattering. The system uses a compact sensing antenna, which has an ultra-wideband operation with directional radiation, and a portable, compact microwave transceiver for signal transmission and data acquisition. The collected data is processed to create a clear image of the brain using an improved back projection algorithm, which is based on a novel effective head permittivity model. The system is verified in realistic simulation and experimental environments using anatomically and electrically realistic human head phantoms. Quantitative and qualitative comparisons between the images from the proposed and existing algorithms demonstrate significant improvements in detection and localization accuracy. The radiation and thermal safety of the system are examined and verified. Initial human tests are conducted on healthy subjects with different head sizes. The reconstructed images are statistically analyzed and absence of false positive results indicate the efficacy of the proposed system in future preclinical trials.

CacyBP/SIP promotes the proliferation of colon cancer cells

PubMed Central

Chen, Xiong; Wang, Jun; Lu, Yuanyuan; Zhang, Faming; Liu, Zhengxiong; Lei, Ting; Fan, Daiming

2017-01-01

CacyBP/SIP is a component of the ubiquitin pathway and is overexpressed in several transformed tumor tissues, including colon cancer, which is one of the most common cancers worldwide. It is unknown whether CacyBP/SIP promotes the proliferation of colon cancer cells. This study examined the expression level, subcellular localization, and binding activity of CacyBP/SIP in human colon cancer cells in the presence and absence of the hormone gastrin. We found that CacyBP/SIP was expressed in a high percentage of colon cancer cells, but not in normal colonic surface epithelium. CacyBP/SIP promoted the cell proliferation of colon cancer cells under both basal and gastrin stimulated conditions as shown by knockdown studies. Gastrin stimulation triggered the translocation of CacyBP/SIP to the nucleus, and enhanced interaction between CacyBP/SIP and SKP1, a key component of ubiquitination pathway which further mediated the proteasome-dependent degradation of p27kip1 protein. The gastrin induced reduction in p27kip1 was prevented when cells were treated with the proteasome inhibitor MG132. These results suggest that CacyBP/SIP may be promoting growth of colon cancer cells by enhancing ubiquitin-mediated degradation of p27kip1. PMID:28196083

Algorithms for Disconnected Diagrams in Lattice QCD

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

Gambhir, Arjun Singh; Stathopoulos, Andreas; Orginos, Konstantinos

2016-11-01

Computing disconnected diagrams in Lattice QCD (operator insertion in a quark loop) entails the computationally demanding problem of taking the trace of the all to all quark propagator. We first outline the basic algorithm used to compute a quark loop as well as improvements to this method. Then, we motivate and introduce an algorithm based on the synergy between hierarchical probing and singular value deflation. We present results for the chiral condensate using a 2+1-flavor clover ensemble and compare estimates of the nucleon charges with the basic algorithm.

Dynamic calibration and analysis of crack tip propagation in energetic materials using real-time radiography

NASA Astrophysics Data System (ADS)

Butt, Ali

Crack propagation in a solid rocket motor environment is difficult to measure directly. This experimental and analytical study evaluated the viability of real-time radiography for detecting bore regression and propellant crack propagation speed. The scope included the quantitative interpretation of crack tip velocity from simulated radiographic images of a burning, center-perforated grain and actual real-time radiographs taken on a rapid-prototyped model that dynamically produced the surface movements modeled in the simulation. The simplified motor simulation portrayed a bore crack that propagated radially at a speed that was 10 times the burning rate of the bore. Comparing the experimental image interpretation with the calibrated surface inputs, measurement accuracies were quantified. The average measurements of the bore radius were within 3% of the calibrated values with a maximum error of 7%. The crack tip speed could be characterized with image processing algorithms, but not with the dynamic calibration data. The laboratory data revealed that noise in the transmitted X-Ray intensity makes sensing the crack tip propagation using changes in the centerline transmitted intensity level impractical using the algorithms employed.

A Collaborative Recommend Algorithm Based on Bipartite Community

PubMed Central

Fu, Yuchen; Liu, Quan; Cui, Zhiming

2014-01-01

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database. PMID:24955393

Low-cost, high-speed back-end processing system for high-frequency ultrasound B-mode imaging.

PubMed

Chang, Jin Ho; Sun, Lei; Yen, Jesse T; Shung, K Kirk

2009-07-01

For real-time visualization of the mouse heart (6 to 13 beats per second), a back-end processing system involving high-speed signal processing functions to form and display images has been developed. This back-end system was designed with new signal processing algorithms to achieve a frame rate of more than 400 images per second. These algorithms were implemented in a simple and cost-effective manner with a single field-programmable gate array (FPGA) and software programs written in C++. The operating speed of the back-end system was investigated by recording the time required for transferring an image to a personal computer. Experimental results showed that the back-end system is capable of producing 433 images per second. To evaluate the imaging performance of the back-end system, a complete imaging system was built. This imaging system, which consisted of a recently reported high-speed mechanical sector scanner assembled with the back-end system, was tested by imaging a wire phantom, a pig eye (in vitro), and a mouse heart (in vivo). It was shown that this system is capable of providing high spatial resolution images with fast temporal resolution.

Low-Cost, High-Speed Back-End Processing System for High-Frequency Ultrasound B-Mode Imaging

PubMed Central

Chang, Jin Ho; Sun, Lei; Yen, Jesse T.; Shung, K. Kirk

2009-01-01

For real-time visualization of the mouse heart (6 to 13 beats per second), a back-end processing system involving high-speed signal processing functions to form and display images has been developed. This back-end system was designed with new signal processing algorithms to achieve a frame rate of more than 400 images per second. These algorithms were implemented in a simple and cost-effective manner with a single field-programmable gate array (FPGA) and software programs written in C++. The operating speed of the back-end system was investigated by recording the time required for transferring an image to a personal computer. Experimental results showed that the back-end system is capable of producing 433 images per second. To evaluate the imaging performance of the back-end system, a complete imaging system was built. This imaging system, which consisted of a recently reported high-speed mechanical sector scanner assembled with the back-end system, was tested by imaging a wire phantom, a pig eye (in vitro), and a mouse heart (in vivo). It was shown that this system is capable of providing high spatial resolution images with fast temporal resolution. PMID:19574160

Using memory-efficient algorithm for large-scale time-domain modeling of surface plasmon polaritons propagation in organic light emitting diodes

NASA Astrophysics Data System (ADS)

Zakirov, Andrey; Belousov, Sergei; Valuev, Ilya; Levchenko, Vadim; Perepelkina, Anastasia; Zempo, Yasunari

2017-10-01

We demonstrate an efficient approach to numerical modeling of optical properties of large-scale structures with typical dimensions much greater than the wavelength of light. For this purpose, we use the finite-difference time-domain (FDTD) method enhanced with a memory efficient Locally Recursive non-Locally Asynchronous (LRnLA) algorithm called DiamondTorre and implemented for General Purpose Graphical Processing Units (GPGPU) architecture. We apply our approach to simulation of optical properties of organic light emitting diodes (OLEDs), which is an essential step in the process of designing OLEDs with improved efficiency. Specifically, we consider a problem of excitation and propagation of surface plasmon polaritons (SPPs) in a typical OLED, which is a challenging task given that SPP decay length can be about two orders of magnitude greater than the wavelength of excitation. We show that with our approach it is possible to extend the simulated volume size sufficiently so that SPP decay dynamics is accounted for. We further consider an OLED with periodically corrugated metallic cathode and show how the SPP decay length can be greatly reduced due to scattering off the corrugation. Ultimately, we compare the performance of our algorithm to the conventional FDTD and demonstrate that our approach can efficiently be used for large-scale FDTD simulations with the use of only a single GPGPU-powered workstation, which is not practically feasible with the conventional FDTD.

Exponential propagators for the Schrödinger equation with a time-dependent potential.

PubMed

Bader, Philipp; Blanes, Sergio; Kopylov, Nikita

2018-06-28

We consider the numerical integration of the Schrödinger equation with a time-dependent Hamiltonian given as the sum of the kinetic energy and a time-dependent potential. Commutator-free (CF) propagators are exponential propagators that have shown to be highly efficient for general time-dependent Hamiltonians. We propose new CF propagators that are tailored for Hamiltonians of the said structure, showing a considerably improved performance. We obtain new fourth- and sixth-order CF propagators as well as a novel sixth-order propagator that incorporates a double commutator that only depends on coordinates, so this term can be considered as cost-free. The algorithms require the computation of the action of exponentials on a vector similar to the well-known exponential midpoint propagator, and this is carried out using the Lanczos method. We illustrate the performance of the new methods on several numerical examples.

The power of neural nets

NASA Technical Reports Server (NTRS)

Ryan, J. P.; Shah, B. H.

1987-01-01

Implementation of the Hopfield net which is used in the image processing type of applications where only partial information about the image may be available is discussed. The image classification type of algorithm of Hopfield and other learning algorithms, such as the Boltzmann machine and the back-propagation training algorithm, have many vital applications in space.

76 FR 69712 - Application To Export Electric Energy; BP Energy Company

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-09

... DEPARTMENT OF ENERGY [OE Docket No. EA-315-A] Application To Export Electric Energy; BP Energy.... SUMMARY: BP Energy Company (BP Energy) has applied to renew its authority to transmit electric energy from... BP Energy to transmit electric energy from the United States to Canada as a power marketer for a five...

Stereo Image Dense Matching by Integrating Sift and Sgm Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Y.; Song, Y.; Lu, J.

2018-05-01

Semi-global matching(SGM) performs the dynamic programming by treating the different path directions equally. It does not consider the impact of different path directions on cost aggregation, and with the expansion of the disparity search range, the accuracy and efficiency of the algorithm drastically decrease. This paper presents a dense matching algorithm by integrating SIFT and SGM. It takes the successful matching pairs matched by SIFT as control points to direct the path in dynamic programming with truncating error propagation. Besides, matching accuracy can be improved by using the gradient direction of the detected feature points to modify the weights of the paths in different directions. The experimental results based on Middlebury stereo data sets and CE-3 lunar data sets demonstrate that the proposed algorithm can effectively cut off the error propagation, reduce disparity search range and improve matching accuracy.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

4E-BP1 regulates the differentiation of white adipose tissue.

PubMed

Tsukiyama-Kohara, Kyoko; Katsume, Asao; Kimura, Kazuhiro; Saito, Masayuki; Kohara, Michinori

2013-07-01

4E Binding protein 1 (4E-BP1) suppresses translation initiation. The absence of 4E-BP1 drastically reduces the amount of adipose tissue in mice. To address the role of 4E-BP1 in adipocyte differentiation, we characterized 4E-BP1(-/-) mice in this study. The lack of 4E-BP1 decreased the amount of white adipose tissue and increased the amount of brown adipose tissue. In 4E-BP1(-/-) MEF cells, PPARγ coactivator 1 alpha (PGC-1α) expression increased and exogenous 4E-BP1 expression suppressed PGC-1α expression. The level of 4E-BP1 expression was higher in white adipocytes than in brown adipocytes and showed significantly greater up-regulation in white adipocytes than in brown adipocytes during preadipocyte differentiation into mature adipocytes. The amount of PGC-1α was consistently higher in HB cells (a brown preadipocyte cell line) than in HW cells (a white preadipocyte cell line) during differentiation. Moreover, the ectopic over-expression of 4E-BP1 suppressed PGC-1α expression in white adipocytes, but not in brown adipocytes. Thus, the results of our study indicate that 4E-BP1 may suppress brown adipocyte differentiation and PGC-1α expression in white adipose tissues. © 2013 The Authors Genes to Cells © 2013 by the Molecular Biology Society of Japan and Wiley Publishing Asia Pty Ltd.

Can Cell to Cell Thermal Runaway Propagation be Prevented in a Li-ion Battery Module?

NASA Technical Reports Server (NTRS)

Jeevarajan, Judith; Lopez, Carlos; Orieukwu, Josephat

2014-01-01

Increasing cell spacing decreased adjacent cell damage center dotElectrically connected adjacent cells drained more than physically adjacent cells center dotRadiant barrier prevents propagation when fully installed between BP cells center dotBP cells vent rapidly and expel contents at 100% SOC -Slower vent with flame/smoke at 50% -Thermal runaway event typically occurs at 160 degC center dotLG cells vent but do not expel contents -Thermal runaway event typically occurs at 200 degC center dotSKC LFP modules did not propagate; fuses on negative terminal of cell may provide a benefit in reducing cell to cell damage propagation. New requirement in NASA-Battery Safety Requirements document: JSC 20793 Rev C 5.1.5.1 Requirements - Thermal Runaway Propagation a. For battery designs greater than a 80-Wh energy employing high specific energy cells (greater than 80 watt-hours/kg, for example, lithium-ion chemistries) with catastrophic failure modes, the battery shall be evaluated to ascertain the severity of a worst-case single-cell thermal runaway event and the propensity of the design to demonstrate cell-to-cell propagation in the intended application and environment. NASA has traditionally addressed the threat of thermal runaway incidents in its battery deployments through comprehensive prevention protocols. This prevention-centered approach has included extensive screening for manufacturing defects, as well as robust battery management controls that prevent abuse-induced runaway even in the face of multiple system failures. This focused strategy has made the likelihood of occurrence of such an event highly improbable. b. The evaluation shall include all necessary analysis and test to quantify the severity (consequence) of the event in the intended application and environment as well as to identify design modifications to the battery or the system that could appreciably reduce that severity. In addition to prevention protocols, programs developing battery designs with

Deepwater BP Oil Spill Natural Resource Damage Assessment Update | NOAA

Science.gov Websites

Publications Press Releases Story Archive Home Deepwater BP Oil Spill Natural Resource Damage Assessment Update Deepwater BP Oil Spill Natural Resource Damage Assessment Update share Posted on July 7, 2011 | Assessment and Early Restoration Restoration Area Title: Deepwater BP Oil Spill Natural Resource Damage

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

PubMed Central

Şen, Alper; Gümüşay, M. Ümit; Kavas, Aktül; Bulucu, Umut

2008-01-01

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN. PMID:27873854

Rehabilitation access and effectiveness for persons with back pain: the protocol of a cohort study (REHAB-BP, DRKS00011554).

PubMed

Bethge, Matthias; Mattukat, Kerstin; Fauser, David; Mau, Wilfried

2017-07-14

Back pain is one of the most common chronic diseases in Germany and has a major impact on work ability and social participation. The German Pension Insurance (GPI) is the main provider of medical rehabilitation to improve work ability and prevent disability pensions in Germany. However, over half of the persons granted a disability pension have never used a medical rehabilitation service. Furthermore, evidence on the effects of medical rehabilitation in Germany is inconclusive. Consequently, this study has two aims: first, to determine barriers to using rehabilitation services, and second, to examine the effectiveness of medical rehabilitation in German residents with chronic back pain. In 2017 a postal questionnaire will be sent to 45,000 persons aged 45 to 59 years whose pension insurance contributions are managed by the GPI North or the GPI Central Germany. In 2019 respondents who report back pain in the first survey (n = 5760 expected) will be sent a second questionnaire. Individuals will be eligible for the first survey if they are employed, have neither used nor applied for a rehabilitation programme during the last 4 years and neither received nor applied for a disability pension. The sample will be drawn randomly from the registers of the GPI North (n = 22,500) and the GPI Central Germany (n = 22,500) and stratified by sex and duration of sickness absence benefits. Barriers to rehabilitation services will be related to socio-demographic and social characteristics, pain and attitudes to pain, health and health behaviour, healthcare utilisation, experiences and cognitions about rehabilitation services and job conditions. Propensity score matched analyses will be used to examine the effectiveness of rehabilitation services. Data on use of medical rehabilitation will be extracted from administrative records. The primary outcome is pain disability. Secondary outcomes are pain intensity and days of disability, pain self-efficacy, fear avoidance beliefs

The potential role of CacyBP/SIP in tumorigenesis.

PubMed

Ning, Xiaoxuan; Chen, Yang; Wang, Xiaosu; Li, Qiaoneng; Sun, Shiren

2016-08-01

Calcyclin-binding protein/Siah-1-interacting protein (CacyBP/SIP) was initially described as a binding partner of S100A6 in the Ehrlich ascites tumor cells and later as a Siah-1-interacting protein. This 30 kDa protein includes three domains and is involved in cell proliferation, differentiation, cytoskeletal rearrangement, and transcriptional regulation via binding to various proteins. Studies have also shown that the CacyBP/SIP is a critical protein in tumorigenesis. But, its promotion or suppression of cancer progression may depend on the cell type. In this review, the biological characteristics and target proteins of CacyBP/SIP have been described. Moreover, the exact role of CacyBP/SIP in various cancers is discussed.

E258K HCM-causing mutation in cardiac MyBP-C reduces contractile force and accelerates twitch kinetics by disrupting the cMyBP-C and myosin S2 interaction.

PubMed

De Lange, Willem J; Grimes, Adrian C; Hegge, Laura F; Spring, Alexander M; Brost, Taylor M; Ralphe, J Carter

2013-09-01

Mutations in cardiac myosin binding protein C (cMyBP-C) are prevalent causes of hypertrophic cardiomyopathy (HCM). Although HCM-causing truncation mutations in cMyBP-C are well studied, the growing number of disease-related cMyBP-C missense mutations remain poorly understood. Our objective was to define the primary contractile effect and molecular disease mechanisms of the prevalent cMyBP-C E258K HCM-causing mutation in nonremodeled murine engineered cardiac tissue (mECT). Wild-type and human E258K cMyBP-C were expressed in mECT lacking endogenous mouse cMyBP-C through adenoviral-mediated gene transfer. Expression of E258K cMyBP-C did not affect cardiac cell survival and was appropriately incorporated into the cardiac sarcomere. Functionally, expression of E258K cMyBP-C caused accelerated contractile kinetics and severely compromised twitch force amplitude in mECT. Yeast two-hybrid analysis revealed that E258K cMyBP-C abolished interaction between the N terminal of cMyBP-C and myosin heavy chain sub-fragment 2 (S2). Furthermore, this mutation increased the affinity between the N terminal of cMyBP-C and actin. Assessment of phosphorylation of three serine residues in cMyBP-C showed that aberrant phosphorylation of cMyBP-C is unlikely to be responsible for altering these interactions. We show that the E258K mutation in cMyBP-C abolishes interaction between N-terminal cMyBP-C and myosin S2 by directly disrupting the cMyBP-C-S2 interface, independent of cMyBP-C phosphorylation. Similar to cMyBP-C ablation or phosphorylation, abolition of this inhibitory interaction accelerates contractile kinetics. Additionally, the E258K mutation impaired force production of mECT, which suggests that in addition to the loss of physiological function, this mutation disrupts contractility possibly by tethering the thick and thin filament or acting as an internal load.

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation

PubMed Central

Yu, Hongyi

2018-01-01

A novel geolocation architecture, termed “Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)” is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér–Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML. PMID:29562601

Direct Position Determination of Unknown Signals in the Presence of Multipath Propagation.

PubMed

Du, Jianping; Wang, Ding; Yu, Wanting; Yu, Hongyi

2018-03-17

A novel geolocation architecture, termed "Multiple Transponders and Multiple Receivers for Multiple Emitters Positioning System (MTRE)" is proposed in this paper. Existing Direct Position Determination (DPD) methods take advantage of a rather simple channel assumption (line of sight channels with complex path attenuations) and a simplified MUltiple SIgnal Classification (MUSIC) algorithm cost function to avoid the high dimension searching. We point out that the simplified assumption and cost function reduce the positioning accuracy because of the singularity of the array manifold in a multi-path environment. We present a DPD model for unknown signals in the presence of Multi-path Propagation (MP-DPD) in this paper. MP-DPD adds non-negative real path attenuation constraints to avoid the mistake caused by the singularity of the array manifold. The Multi-path Propagation MUSIC (MP-MUSIC) method and the Active Set Algorithm (ASA) are designed to reduce the dimension of searching. A Multi-path Propagation Maximum Likelihood (MP-ML) method is proposed in addition to overcome the limitation of MP-MUSIC in the sense of a time-sensitive application. An iterative algorithm and an approach of initial value setting are given to make the MP-ML time consumption acceptable. Numerical results validate the performances improvement of MP-MUSIC and MP-ML. A closed form of the Cramér-Rao Lower Bound (CRLB) is derived as a benchmark to evaluate the performances of MP-MUSIC and MP-ML.

Surface Water Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

A Survey of Symplectic and Collocation Integration Methods for Orbit Propagation

NASA Technical Reports Server (NTRS)

Jones, Brandon A.; Anderson, Rodney L.

2012-01-01

Demands on numerical integration algorithms for astrodynamics applications continue to increase. Common methods, like explicit Runge-Kutta, meet the orbit propagation needs of most scenarios, but more specialized scenarios require new techniques to meet both computational efficiency and accuracy needs. This paper provides an extensive survey on the application of symplectic and collocation methods to astrodynamics. Both of these methods benefit from relatively recent theoretical developments, which improve their applicability to artificial satellite orbit propagation. This paper also details their implementation, with several tests demonstrating their advantages and disadvantages.

Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction.

PubMed

Araújo, Ricardo de A

2010-12-01

This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

A weighted belief-propagation algorithm for estimating volume-related properties of random polytopes

NASA Astrophysics Data System (ADS)

Font-Clos, Francesc; Massucci, Francesco Alessandro; Pérez Castillo, Isaac

2012-11-01

In this work we introduce a novel weighted message-passing algorithm based on the cavity method for estimating volume-related properties of random polytopes, properties which are relevant in various research fields ranging from metabolic networks, to neural networks, to compressed sensing. We propose, as opposed to adopting the usual approach consisting in approximating the real-valued cavity marginal distributions by a few parameters, using an algorithm to faithfully represent the entire marginal distribution. We explain various alternatives for implementing the algorithm and benchmarking the theoretical findings by showing concrete applications to random polytopes. The results obtained with our approach are found to be in very good agreement with the estimates produced by the Hit-and-Run algorithm, known to produce uniform sampling.

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used

Numerical simulation and experimental validation of Lamb wave propagation behavior in composite plates

NASA Astrophysics Data System (ADS)

Kim, Sungwon; Uprety, Bibhisha; Mathews, V. John; Adams, Daniel O.

2015-03-01

Structural Health Monitoring (SHM) based on Acoustic Emission (AE) is dependent on both the sensors to detect an impact event as well as an algorithm to determine the impact location. The propagation of Lamb waves produced by an impact event in thin composite structures is affected by several unique aspects including material anisotropy, ply orientations, and geometric discontinuities within the structure. The development of accurate numerical models of Lamb wave propagation has important benefits towards the development of AE-based SHM systems for impact location estimation. Currently, many impact location algorithms utilize the time of arrival or velocities of Lamb waves. Therefore the numerical prediction of characteristic wave velocities is of great interest. Additionally, the propagation of the initial symmetric (S0) and asymmetric (A0) wave modes is important, as these wave modes are used for time of arrival estimation. In this investigation, finite element analyses were performed to investigate aspects of Lamb wave propagation in composite plates with active signal excitation. A comparative evaluation of two three-dimensional modeling approaches was performed, with emphasis placed on the propagation and velocity of both the S0 and A0 wave modes. Results from numerical simulations are compared to experimental results obtained from active AE testing. Of particular interest is the directional dependence of Lamb waves in quasi-isotropic carbon/epoxy composite plates. Numerical and experimental results suggest that although a quasi-isotropic composite plate may have the same effective elastic modulus in all in-plane directions, the Lamb wave velocity may have some directional dependence. Further numerical analyses were performed to investigate Lamb wave propagation associated with circular cutouts in composite plates.

Temperature Effects and Compensation-Control Methods

PubMed Central

Xia, Dunzhu; Chen, Shuling; Wang, Shourong; Li, Hongsheng

2009-01-01

In the analysis of the effects of temperature on the performance of microgyroscopes, it is found that the resonant frequency of the microgyroscope decreases linearly as the temperature increases, and the quality factor changes drastically at low temperatures. Moreover, the zero bias changes greatly with temperature variations. To reduce the temperature effects on the microgyroscope, temperature compensation-control methods are proposed. In the first place, a BP (Back Propagation) neural network and polynomial fitting are utilized for building the temperature model of the microgyroscope. Considering the simplicity and real-time requirements, piecewise polynomial fitting is applied in the temperature compensation system. Then, an integral-separated PID (Proportion Integration Differentiation) control algorithm is adopted in the temperature control system, which can stabilize the temperature inside the microgyrocope in pursuing its optimal performance. Experimental results reveal that the combination of microgyroscope temperature compensation and control methods is both realizable and effective in a miniaturized microgyroscope prototype. PMID:22408509

NEOPROP: A NEO Propagator for Space Situational Awareness

NASA Astrophysics Data System (ADS)

Zuccarelli, Valentino; Bancelin, David; Weikert, Sven; Thuillot, William; Hestroffer, Daniel; Yabar Valle, Celia; Koschny, Detlef

2013-09-01

Analytical Module makes use of analytical algorithms in order to rapidly assess the impact risk of a NEO. It is responsible for the preliminary analysis. Orbit Determination algorithms, as the Gauss and the Linear Least Squares (LLS) methods, will determine the initial state (from MPC observations), along with its uncertainty, and the MOID of the NEO (analytically).2. The Numerical Module makes use of numerical algorithms in order to refine and to better assess the impact probabilities. The initial state provided by the orbit determination process will be used to numerically propagate the trajectory. The numerical propagation can be run in two modes: one faster ("fast analysis"), in order to get a fast evaluation of the trajectory and one more precise ("complete analysis") taking into consideration more detailed perturbation models. Moreover, a configurable number of Virtual Asteroids (VAs) will be numerically propagated in order to determine the Earth closest approach. This new "MOID" computation differs from the analytical one since it takes into consideration the full dynamics of the problem.

Aspect Ratio of Receiver Node Geometry based Indoor WLAN Propagation Model

NASA Astrophysics Data System (ADS)

Naik, Udaykumar; Bapat, Vishram N.

2017-08-01

This paper presents validation of indoor wireless local area network (WLAN) propagation model for varying rectangular receiver node geometry. The rectangular client node configuration is a standard node arrangement in computer laboratories of academic institutes and research organizations. The model assists to install network nodes for the better signal coverage. The proposed model is backed by wide ranging real time received signal strength measurements at 2.4 GHz. The shadow fading component of signal propagation under realistic indoor environment is modelled with the dependency on varying aspect ratio of the client node geometry. The developed new model is useful in predicting indoor path loss for IEEE 802.11b/g WLAN. The new model provides better performance in comparison to well known International Telecommunication Union and free space propagation models. It is shown that the proposed model is simple and can be a useful tool for indoor WLAN node deployment planning and quick method for the best utilisation of the office space.