Hidden Neural Networks: A Framework for HMM/NN Hybrids

DEFF Research Database (Denmark)

Riis, Søren Kamaric; Krogh, Anders Stærmose

1997-01-01

This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...

Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR

OpenAIRE

Hagen, Astrid; Morris, Andrew

2000-01-01

In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood--based MGMs...

Damage evaluation by a guided wave-hidden Markov model based method

Science.gov (United States)

Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin

2016-02-01

Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

Using features of local densities, statistics and HMM toolkit (HTK for offline Arabic handwriting text recognition

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El Moubtahij Hicham

2017-12-01

Full Text Available This paper presents an analytical approach of an offline handwritten Arabic text recognition system. It is based on the Hidden Markov Models (HMM Toolkit (HTK without explicit segmentation. The first phase is preprocessing, where the data is introduced in the system after quality enhancements. Then, a set of characteristics (features of local densities and features statistics are extracted by using the technique of sliding windows. Subsequently, the resulting feature vectors are injected to the Hidden Markov Model Toolkit (HTK. The simple database “Arabic-Numbers” and IFN/ENIT are used to evaluate the performance of this system. Keywords: Hidden Markov Models (HMM Toolkit (HTK, Sliding windows

HMM-ModE – Improved classification using profile hidden Markov models by optimising the discrimination threshold and modifying emission probabilities with negative training sequences

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Nandi Soumyadeep

2007-03-01

Full Text Available Abstract Background Profile Hidden Markov Models (HMM are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives. Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. Results The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors

An HMM posterior decoder for sequence feature prediction that includes homology information

DEFF Research Database (Denmark)

Käll, Lukas; Krogh, Anders Stærmose; Sonnhammer, Erik L. L.

2005-01-01

Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabil......Motivation: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. Results: We present here a general hidden Markov model (HMM) decoding algorithm that combines......://phobius.cgb.ki.se/poly.html . An implementation of the algorithm is available on request from the authors....

Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

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Jihyun Kim

2017-01-01

Full Text Available Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

Research study on harmonized molecular materials (HMM); Bunshi kyocho zairyo ni kansuru chosa kenkyu

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-03-01

As functional material to satisfy various needs for environmental harmonization and efficient conversion for information-oriented and aging societies, HMM were surveyed. Living bodies effectively carry out transmission/processing of information, and transport/conversion of substances, and these functions are based on harmonization between organic molecules, and between those and metal or inorganic ones. HMM is a key substance to artificially realize these bio-related functions. Its R & D aims at (1) Making a breakthrough in production process based on innovation of material separation/conversion technology, (2) Contribution to an information-oriented society by high-efficiency devices, and (3) Growth of a functional bio-material industry. HMM is classified into three categories: (1) Assembly materials such as organic ultra-thin films (LB film, self-organizing film), and organic/inorganic hybrid materials for optoelectronics, sensors and devices, (2) Mesophase materials such as functional separation membrane and photo-conductive material, and (3) Microporous materials such as synthetic catalyst using guest/host materials. 571 refs., 88 figs., 21 tabs.

Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

Science.gov (United States)

Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

2018-06-01

Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

Efficient view based 3-D object retrieval using Hidden Markov Model

Science.gov (United States)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

A Method for Driving Route Predictions Based on Hidden Markov Model

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Ning Ye

2015-01-01

Full Text Available We present a driving route prediction method that is based on Hidden Markov Model (HMM. This method can accurately predict a vehicle’s entire route as early in a trip’s lifetime as possible without inputting origins and destinations beforehand. Firstly, we propose the route recommendation system architecture, where route predictions play important role in the system. Secondly, we define a road network model, normalize each of driving routes in the rectangular coordinate system, and build the HMM to make preparation for route predictions using a method of training set extension based on K-means++ and the add-one (Laplace smoothing technique. Thirdly, we present the route prediction algorithm. Finally, the experimental results of the effectiveness of the route predictions that is based on HMM are shown.

Spotting handwritten words and REGEX using a two stage BLSTM-HMM architecture

Science.gov (United States)

Bideault, Gautier; Mioulet, Luc; Chatelain, Clément; Paquet, Thierry

2015-01-01

In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.

A state-based probabilistic model for tumor respiratory motion prediction

International Nuclear Information System (INIS)

Kalet, Alan; Sandison, George; Schmitz, Ruth; Wu Huanmei

2010-01-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more

Reliability-Based Modeling of Moisture and Load-Duration Effects

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Svensson, Staffan

2005-01-01

Load duration effects with respect to reduction of load bearing capacity are very important for structural timber. This paper describes how the load duration effects combined with moisture content and variations c an be determined on basis of simulation of realizations of the time varying load...... and moisture processes. Permanent and snow load sand moisture variations are considered and stochastic models are formulated in accordance with the load models in the Danish structural codes. A damage accumulation model based on fracture mechanics that accounts for both load duration and moisture effects...... is derived. The parameters in the model are fitted to data relevant for Nordic structural timber using the Maximum Likelihood method. The probability of failure as function of time is estimated for representative limit states based on: a) short term strength and b) long term damage accumulation...

A method for identifying gas-liquid two-phase flow patterns on the basis of wavelet packet multi-scale information entropy and HMM

International Nuclear Information System (INIS)

Zhou Yunlong; Zhang Xueqing; Gao Yunpeng; Cheng Yue

2009-01-01

For studying flow regimes of gas/liquid two-phase in a vertical upward pipe, the conductance fluctuation information of four typical flow regimes was collected by a measuring the system with self-made multiple conductivity probes. Owing to the non-stationarity of conductance fluctuation signals of gas-liquid two-phase flow, a kind of' flow regime identification method based on wavelet packet Multi-scale Information Entropy and Hidden Markov Model (HMM) was put forward. First of all, the collected conductance fluctuation signals were decomposed into eight different frequency bands signals. Secondly, the wavelet packet multi-scale information entropy of different frequency bands signals were regarded as the input characteristic vectors of all states HMM which had been trained. In the end the regime identification of' the gas-liquid two-phase flow could be performed. The study showed that the method that HMM was applied to identify the flow regime was superior to the one that BP neural network was used, and the results proved that the method was efficient and feasible. (authors)

Bearing Performance Degradation Assessment Using Linear Discriminant Analysis and Coupled HMM

International Nuclear Information System (INIS)

Liu, T; Chen, J; Zhou, X N; Xiao, W B

2012-01-01

Bearing is one of the most important units in rotary machinery, its performance may vary significantly under different working stages. Thus it is critical to choose the most effective features for bearing performance degradation prediction. Linear Discriminant Analysis (LDA) is a useful method in finding few feature's dimensions that best discriminate a set of features extracted from original vibration signals. Another challenge in bearing performance degradation is how to build a model to recognize the different conditions with the data coming from different monitoring channels. In this paper, coupled hidden Markov models (CHMM) is presented to model interacting processes which can overcome the defections of the HMM. Because the input data in CHMM are collected by several sensors, and the interacting information can be fused by coupled modalities, it is more effective than HMM which used only one state chain. The model can be used in estimating the bearing performance degradation states according to several observation data. When becoming degradation pattern recognition, the new observation features should be input into the pre-trained CHMM and calculate the performance index (PI) of the outputs, the changing of PI could be used to describe the different degradation level of the bearings. The results show that PI will decline with the increase of the bearing degradation. Assessment results of the whole life time experimental bearing signals validate the feasibility and effectiveness of this method.

Neuroevolution Mechanism for Hidden Markov Model

Directory of Open Access Journals (Sweden)

Nabil M. Hewahi

2011-12-01

Full Text Available Hidden Markov Model (HMM is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language
processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by
applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.

HMM-based lexicon-driven and lexicon-free word recognition for online handwritten Indic scripts.

Science.gov (United States)

Bharath, A; Madhvanath, Sriganesh

2012-04-01

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

Online adaptive learning of Left-Right Continuous HMM for bearings condition assessment

International Nuclear Information System (INIS)

Cartella, F; Liu, T; Meganck, S; Lemeire, J; Sahli, H

2012-01-01

Standard Hidden Markov Models (HMMs) approaches used for condition assessment of bearings assume that all the possible system states are fixed and known a priori and that training data from all of the associated states are available. Moreover, the training procedure is performed offline, and only once at the beginning, with the available training set. These assumptions significantly impede component diagnosis applications when all of the possible states of the system are not known in advance or environmental factors or operative conditions change during the tool's usage. The method introduced in this paper overcomes the above limitations and proposes an approach to detect unknown degradation modalities using a Left-Right Continuous HMM with a variable state space. The proposed HMM is combined with Change Point Detection algorithms to (i) estimate, from historical observations, the initial number of the model's states, as well as to perform an initial guess of the parameters, and (ii) to adaptively recognize new states and, consequently, adjust the model parameters during monitoring. The approach has been tested using real monitoring data taken from the NASA benchmark repository. A comparative study with state of the art techniques shows improvements in terms of reduction of the training procedure iterations, and early detection of unknown states.

Explorations in the History of Machines and Mechanisms : Proceedings of HMM2012

CERN Document Server

Ceccarelli, Marco

2012-01-01

This book contains the proceedings of HMM2012, the 4th International Symposium on Historical Developments in the field of Mechanism and Machine Science (MMS). These proceedings cover recent research concerning all aspects of the development of MMS from antiquity until the present and its historiography: machines, mechanisms, kinematics, dynamics, concepts and theories, design methods, collections of methods, collections of models, institutions and biographies.

A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model

Directory of Open Access Journals (Sweden)

Jason Chin-Tiong Chan

2018-01-01

Full Text Available The optimal state sequence of a generalized High-Order Hidden Markov Model (HHMM is tracked from a given observational sequence using the classical Viterbi algorithm. This classical algorithm is based on maximum likelihood criterion. We introduce an entropy-based Viterbi algorithm for tracking the optimal state sequence of a HHMM. The entropy of a state sequence is a useful quantity, providing a measure of the uncertainty of a HHMM. There will be no uncertainty if there is only one possible optimal state sequence for HHMM. This entropy-based decoding algorithm can be formulated in an extended or a reduction approach. We extend the entropy-based algorithm for computing the optimal state sequence that was developed from a first-order to a generalized HHMM with a single observational sequence. This extended algorithm performs the computation exponentially with respect to the order of HMM. The computational complexity of this extended algorithm is due to the growth of the model parameters. We introduce an efficient entropy-based decoding algorithm that used reduction approach, namely, entropy-based order-transformation forward algorithm (EOTFA to compute the optimal state sequence of any generalized HHMM. This EOTFA algorithm involves a transformation of a generalized high-order HMM into an equivalent first-order HMM and an entropy-based decoding algorithm is developed based on the equivalent first-order HMM. This algorithm performs the computation based on the observational sequence and it requires OTN~2 calculations, where N~ is the number of states in an equivalent first-order model and T is the length of observational sequence.

A Bayesian Approach for Structural Learning with Hidden Markov Models

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Cen Li

2002-01-01

Full Text Available Hidden Markov Models(HMM have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.

QRS complex detection based on continuous density hidden Markov models using univariate observations

Science.gov (United States)

Sotelo, S.; Arenas, W.; Altuve, M.

2018-04-01

In the electrocardiogram (ECG), the detection of QRS complexes is a fundamental step in the ECG signal processing chain since it allows the determination of other characteristics waves of the ECG and provides information about heart rate variability. In this work, an automatic QRS complex detector based on continuous density hidden Markov models (HMM) is proposed. HMM were trained using univariate observation sequences taken either from QRS complexes or their derivatives. The detection approach is based on the log-likelihood comparison of the observation sequence with a fixed threshold. A sliding window was used to obtain the observation sequence to be evaluated by the model. The threshold was optimized by receiver operating characteristic curves. Sensitivity (Sen), specificity (Spc) and F1 score were used to evaluate the detection performance. The approach was validated using ECG recordings from the MIT-BIH Arrhythmia database. A 6-fold cross-validation shows that the best detection performance was achieved with 2 states HMM trained with QRS complexes sequences (Sen = 0.668, Spc = 0.360 and F1 = 0.309). We concluded that these univariate sequences provide enough information to characterize the QRS complex dynamics from HMM. Future works are directed to the use of multivariate observations to increase the detection performance.

Research on light rail electric load forecasting based on ARMA model

Science.gov (United States)

Huang, Yifan

2018-04-01

The article compares a variety of time series models and combines the characteristics of power load forecasting. Then, a light load forecasting model based on ARMA model is established. Based on this model, a light rail system is forecasted. The prediction results show that the accuracy of the model prediction is high.

Simple Models for Model-based Portfolio Load Balancing Controller Synthesis

DEFF Research Database (Denmark)

Edlund, Kristian Skjoldborg; Mølbak, Tommy; Bendtsen, Jan Dimon

2010-01-01

of generation units existing in an electrical power supply network, for instance in model-based predictive control or declarative control schemes. We focus on the effectuators found in the Danish power system. In particular, the paper presents models for boiler load, district heating, condensate throttling...

Comparison of HMM and DTW methods in automatic recognition of pathological phoneme pronunciation

OpenAIRE

Wielgat, Robert; Zielinski, Tomasz P.; Swietojanski, Pawel; Zoladz, Piotr; Król, Daniel; Wozniak, Tomasz; Grabias, Stanislaw

2007-01-01

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) are used as classifiers in the presented research. Obtained resul...

HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data

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Maher Christopher A

2010-07-01

Full Text Available Abstract Background Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP with next-generation sequencing, (ChIP-Seq. This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method. Results Here we introduce HPeak, a Hidden Markov model (HMM-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage. Conclusions Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.

A stochastic HMM-based forecasting model for fuzzy time series.

Science.gov (United States)

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

A semi-analytical bearing model considering outer race flexibility for model based bearing load monitoring

Science.gov (United States)

Kerst, Stijn; Shyrokau, Barys; Holweg, Edward

2018-05-01

This paper proposes a novel semi-analytical bearing model addressing flexibility of the bearing outer race structure. It furthermore presents the application of this model in a bearing load condition monitoring approach. The bearing model is developed as current computational low cost bearing models fail to provide an accurate description of the more and more common flexible size and weight optimized bearing designs due to their assumptions of rigidity. In the proposed bearing model raceway flexibility is described by the use of static deformation shapes. The excitation of the deformation shapes is calculated based on the modelled rolling element loads and a Fourier series based compliance approximation. The resulting model is computational low cost and provides an accurate description of the rolling element loads for flexible outer raceway structures. The latter is validated by a simulation-based comparison study with a well-established bearing simulation software tool. An experimental study finally shows the potential of the proposed model in a bearing load monitoring approach.

Intelligent harmonic load model based on neural networks

Science.gov (United States)

Ji, Pyeong-Shik; Lee, Dae-Jong; Lee, Jong-Pil; Park, Jae-Won; Lim, Jae-Yoon

2007-12-01

In this study, we developed a RBFNs(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method implemented by using harmonic information as well as fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. The RBFNs have certain advantage such as simple structure and rapid computation ability compared with multilayer perceptron which is extensively applied for load modeling. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynominal 2nd equation method, MLP and RBF without considering harmonic components.

Segment-based acoustic models for continuous speech recognition

Science.gov (United States)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

Directory of Open Access Journals (Sweden)

Lokesh Selvaraj

2014-01-01

Full Text Available Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO is suggested. The suggested methodology contains four stages, namely, (i denoising, (ii feature mining (iii, vector quantization, and (iv IPSO based hidden Markov model (HMM technique (IP-HMM. At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC, mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Physical and JIT Model Based Hybrid Modeling Approach for Building Thermal Load Prediction

Science.gov (United States)

Iino, Yutaka; Murai, Masahiko; Murayama, Dai; Motoyama, Ichiro

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

Model-Based Load Estimation for Predictive Condition Monitoring of Wind Turbines

DEFF Research Database (Denmark)

Perisic, Nevena; Pederen, Bo Juul; Grunnet, Jacob Deleuran

signal is performed online, and a Load Indicator Signal (LIS) is formulated as a ratio between current estimated accumulated fatigue loads and its expected value based only on a priori knowledge (WTG dynamics and wind climate). LOT initialisation is based on a priori knowledge and can be obtained using...... programme for pre-maintenance actions. The performance of LOT is demonstrated by applying it to one of the most critical WTG components, the gearbox. Model-based load CMS for gearbox requires only standard WTG SCADA data. Direct measuring of gearbox fatigue loads requires high cost and low reliability...... measurement equipment. Thus, LOT can significantly reduce the price of load monitoring....

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

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Jianzhou Wang

2014-01-01

Full Text Available Swarm intelligence (SI is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS as well as the singular spectrum analysis (SSA, time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA and support vector regression (SVR in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance.

A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

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Juri Taborri

2014-09-01

Full Text Available In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98 for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95 when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building

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Younghoon Kwak

2018-04-01

Full Text Available In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined in residential buildings. Experiments were designed to be applicable to general residential buildings and four types of commonly used appliances were selected to verify the method. The method assumes that loads are disaggregated and measured by a single primary meter. Following the metering of household appliances and an analysis of the usage patterns of each type, values of electric current were entered into a Hidden Markov Model (HMM to formulate predictions. Thereafter, the HMM repeatedly performed to output the predicted data close to the measured data, while errors between predicted and the measured data were evaluated to determine whether they met tolerance. When the method was examined for 4 days, matching rates in accordance with the load disaggregation outcomes of the household appliances (i.e., laptop, refrigerator, TV, and microwave were 0.994, 0.992, 0.982, and 0.988, respectively. The proposed method can provide insights into how and where within such buildings energy is consumed. As a result, effective and systematic energy saving measures can be derived even in buildings in which monitoring sensors and measurement equipment are not installed.

Implementation of a Tour Guide Robot System Using RFID Technology and Viterbi Algorithm-Based HMM for Speech Recognition

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Neng-Sheng Pai

2014-01-01

Full Text Available This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. For speech recognition, the speech signals were captured by short-time processing. The speaker first recorded the isolated words for the robot to create speech database of specific speakers. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC. Then, the Hidden Markov Model (HMM was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot.

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

Science.gov (United States)

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

Genetic Algorithms Principles Towards Hidden Markov Model

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Nabil M. Hewahi

2011-10-01

Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

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An Luo

2017-10-01

Full Text Available Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Physics Based Model for Cryogenic Chilldown and Loading. Part I: Algorithm

Science.gov (United States)

Luchinsky, Dmitry G.; Smelyanskiy, Vadim N.; Brown, Barbara

2014-01-01

We report the progress in the development of the physics based model for cryogenic chilldown and loading. The chilldown and loading is model as fully separated non-equilibrium two-phase flow of cryogenic fluid thermally coupled to the pipe walls. The solution follow closely nearly-implicit and semi-implicit algorithms developed for autonomous control of thermal-hydraulic systems developed by Idaho National Laboratory. A special attention is paid to the treatment of instabilities. The model is applied to the analysis of chilldown in rapid loading system developed at NASA-Kennedy Space Center. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The numerical predictions are in reasonable agreement with the experimental time traces. The obtained results pave the way to the development of autonomous loading operation on the ground and space.

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

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Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan, Shu; Lee, Wei-Jen [Energy Systems Research Center, The University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen, Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan)

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma. (author)

Machine learning based switching model for electricity load forecasting

Energy Technology Data Exchange (ETDEWEB)

Fan Shu [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States); Chen Luonan [Department of Electronics, Information and Communication Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-0013 (Japan); Lee, Weijen [Energy Systems Research Center, University of Texas at Arlington, 416 S. College Street, Arlington, TX 76019 (United States)], E-mail: wlee@uta.edu

2008-06-15

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma.

Machine learning based switching model for electricity load forecasting

International Nuclear Information System (INIS)

Fan Shu; Chen Luonan; Lee, Weijen

2008-01-01

In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: Bayesian clustering by dynamics (BCD) and support vector regression (SVR), this paper proposes a novel forecasting model for day ahead electricity load forecasting. The proposed model adopts an integrated architecture to handle the non-stationarity of time series. Firstly, a BCD classifier is applied to cluster the input data set into several subsets by the dynamics of the time series in an unsupervised manner. Then, groups of SVRs are used to fit the training data of each subset in a supervised way. The effectiveness of the proposed model is demonstrated with actual data taken from the New York ISO and the Western Farmers Electric Cooperative in Oklahoma

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

Science.gov (United States)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

Cyclic loading of thick vessels based on the Prager and Armstrong-Frederick kinematic hardening models

International Nuclear Information System (INIS)

Mahbadi, H.; Eslami, M.R.

2006-01-01

The aim of this paper is to relate the type of stress category in cyclic loading to ratcheting or shakedown behaviour of the structure. The kinematic hardening theory of plasticity based on the Prager and Armstrong-Frederick models is used to evaluate the cyclic loading behaviour of thick spherical and cylindrical vessels under load and deformation controlled stresses. It is concluded that kinematic hardening based on the Prager model under load and deformation controlled conditions, excluding creep, results in shakedown or reversed plasticity for spherical and cylindrical vessels with the isotropy assumption of the tension/compression curve. Under an anisotropy assumption of the tension/compression curve, this model predicts ratcheting. On the other hand, the Armstrong-Frederick model predicts ratcheting under load controlled cyclic loading and reversed plasticity for deformation controlled stress. The interesting conclusion is that the Armstrong-Frederick model is well capable to predict the experimental data under the assumed type of stresses, wherever experimental data are available

Measurements and modelling of base station power consumption under real traffic loads.

Science.gov (United States)

Lorincz, Josip; Garma, Tonko; Petrovic, Goran

2012-01-01

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.

Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads

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Goran Petrovic

2012-03-01

Full Text Available Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications and UMTS (Universal Mobile Telecommunications System base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.

Load management: Model-based control of aggregate power for populations of thermostatically controlled loads

International Nuclear Information System (INIS)

Perfumo, Cristian; Kofman, Ernesto; Braslavsky, Julio H.; Ward, John K.

2012-01-01

Highlights: ► Characterisation of power response of a population of air conditioners. ► Implementation of demand side management on a group of air conditioners. ► Design of a controller for the power output of a group of air conditioners. ► Quantification of comfort impact of demand side management. - Abstract: Large groups of electrical loads can be controlled as a single entity to reduce their aggregate power demand in the electricity network. This approach, known as load management (LM) or demand response, offers an alternative to the traditional paradigm in the electricity market, where matching supply and demand is achieved solely by regulating how much generation is dispatched. Thermostatically controlled loads (TCLs), such as air conditioners (ACs) and fridges, are particularly suitable for LM, which can be implemented using feedback control techniques to regulate their aggregate power. To achieve high performance, such feedback control techniques require an accurate mathematical model of the TCL aggregate dynamics. Although such models have been developed, they appear too complex to be effectively used in control design. In this paper we develop a mathematical model aimed at the design of a model-based feedback control strategy. The proposed model analytically characterises the aggregate power response of a population of ACs to a simultaneous step change in temperature set points. Based on this model, we then derive, and completely parametrise in terms of the ACs ensemble properties, a reduced-order mathematical model to design an internal-model controller that regulates aggregate power by broadcasting temperature set-point offset changes. The proposed controller achieves high LM performance provided the ACs are equipped with high resolution thermostats. With coarser resolution thermostats, which are typical in present commercial and residential ACs, performance deteriorates significantly. This limitation is overcome by subdividing the population

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

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Anand Krishnan Prakash

2018-04-01

Full Text Available Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University for both short- and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error.

Mobile Application Identification based on Hidden Markov Model

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Yang Xinyan

2018-01-01

Full Text Available With the increasing number of mobile applications, there has more challenging network management tasks to resolve. Users also face security issues of the mobile Internet application when enjoying the mobile network resources. Identifying applications that correspond to network traffic can help network operators effectively perform network management. The existing mobile application recognition technology presents new challenges in extensibility and applications with encryption protocols. For the existing mobile application recognition technology, there are two problems, they can not recognize the application which using the encryption protocol and their scalability is poor. In this paper, a mobile application identification method based on Hidden Markov Model(HMM is proposed to extract the defined statistical characteristics from different network flows generated when each application starting. According to the time information of different network flows to get the corresponding time series, and then for each application to be identified separately to establish the corresponding HMM model. Then, we use 10 common applications to test the method proposed in this paper. The test results show that the mobile application recognition method proposed in this paper has a high accuracy and good generalization ability.

Short-Term Fuzzy Load Forecasting Model Using Genetic–Fuzzy and Ant Colony–Fuzzy Knowledge Base Optimization

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Murat Luy

2018-05-01

Full Text Available The estimation of hourly electricity load consumption is highly important for planning short-term supply–demand equilibrium in sources and facilities. Studies of short-term load forecasting in the literature are categorized into two groups: classical conventional and artificial intelligence-based methods. Artificial intelligence-based models, especially when using fuzzy logic techniques, have more accurate load estimations when datasets include high uncertainty. However, as the knowledge base—which is defined by expert insights and decisions—gets larger, the load forecasting performance decreases. This study handles the problem that is caused by the growing knowledge base, and improves the load forecasting performance of fuzzy models through nature-inspired methods. The proposed models have been optimized by using ant colony optimization and genetic algorithm (GA techniques. The training and testing processes of the proposed systems were performed on historical hourly load consumption and temperature data collected between 2011 and 2014. The results show that the proposed models can sufficiently improve the performance of hourly short-term load forecasting. The mean absolute percentage error (MAPE of the monthly minimum in the forecasting model, in terms of the forecasting accuracy, is 3.9% (February 2014. The results show that the proposed methods make it possible to work with large-scale rule bases in a more flexible estimation environment.

Compact Acoustic Models for Embedded Speech Recognition

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Lévy Christophe

2009-01-01

Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.

HMMEditor: a visual editing tool for profile hidden Markov model

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Cheng Jianlin

2008-03-01

Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

Development of TTS Engine for Indian Accent using Modified HMM Algorithm

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Sasanko Sekhar Gantayat

2018-03-01

Full Text Available A text-to-speech (TTS system converts normal language text into speech. An intelligent text-to-speech program allows people with visual impairments or reading disabilities, to listen to written works on a home computer. Many computer operating systems and day to day software applications like Adobe Reader have included text-to-speech systems. This paper is presented to show that how HMM can be used as a tool to convert text to speech.

Fatigue Load Modeling and Control for Wind Turbines based on Hysteresis Operators

DEFF Research Database (Denmark)

Barradas Berglind, Jose de Jesus; Wisniewski, Rafal; Soltani, Mohsen

2015-01-01

method based on hysteresis operators, which can be used in control loops. Furthermore, we propose a model predictive control (MPC) strategy that incorporates the online fatigue estimation through the objective function, where the ultimate goal in mind is to reduce the fatigue load of the wind turbine......The focus of this work is on fatigue load modeling and controller design for the wind turbine level. The main purpose is to include a model of the damage effects caused by the fatigue of the wind turbine components in the controller design process. This paper addresses an online fatigue estimation...

Coding with partially hidden Markov models

DEFF Research Database (Denmark)

Forchhammer, Søren; Rissanen, J.

1995-01-01

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general...... 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A forward-backward reestimation of parameters with a redefined backward variable is given for these models and used for estimating the unknown parameters. Proof of convergence of this reestimation is given....... The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt...

An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application

DEFF Research Database (Denmark)

Hauberg, Søren; Sloth, Jakob

2008-01-01

For many years, the hidden Markov model (HMM) has been one of the most popular tools for analysing sequential data. One frequently used special case is the left-right model, in which the order of the hidden states is known. If knowledge of the duration of a state is available it is not possible...... to represent it explicitly with an HMM. Methods for modelling duration with HMM's do exist (Rabiner in Proc. IEEE 77(2):257---286, [1989]), but they come at the price of increased computational complexity. Here we present an efficient and robust algorithm for modelling duration in HMM's, and this algorithm...

Economic MPC based on LPV model for thermostatically controlled loads

DEFF Research Database (Denmark)

Zemtsov, Nikita; Hlava, Jaroslav; Frantsuzova, Galina

2017-01-01

Rapid increase of the renewable energy share in electricity production requires optimization and flexibility of the power consumption side. Thermostatically controlled loads (TCLs) have a large potential for regulation service provision. Economic model predictive control (MPC) is an advanced...... control method which can be used to syncronize the power consumption with undispatchable renewable electricity production. Thermal behavior of TCLs can be described by linear models based on energy balance of the system. In some cases, parameters of the model may be time-varying. In this work, we present...... a modified economic MPC based on linear parameter-varying model. In particular, we provide an exact transformation from a standard economic MPC formulation to a linear program. We assume that the variables influencing the model parameters are known (predictable) for the prediction horizon of the controller...

Bearing Fault Classification Based on Conditional Random Field

Directory of Open Access Journals (Sweden)

Guofeng Wang

2013-01-01

Full Text Available Condition monitoring of rolling element bearing is paramount for predicting the lifetime and performing effective maintenance of the mechanical equipment. To overcome the drawbacks of the hidden Markov model (HMM and improve the diagnosis accuracy, conditional random field (CRF model based classifier is proposed. In this model, the feature vectors sequences and the fault categories are linked by an undirected graphical model in which their relationship is represented by a global conditional probability distribution. In comparison with the HMM, the main advantage of the CRF model is that it can depict the temporal dynamic information between the observation sequences and state sequences without assuming the independence of the input feature vectors. Therefore, the interrelationship between the adjacent observation vectors can also be depicted and integrated into the model, which makes the classifier more robust and accurate than the HMM. To evaluate the effectiveness of the proposed method, four kinds of bearing vibration signals which correspond to normal, inner race pit, outer race pit and roller pit respectively are collected from the test rig. And the CRF and HMM models are built respectively to perform fault classification by taking the sub band energy features of wavelet packet decomposition (WPD as the observation sequences. Moreover, K-fold cross validation method is adopted to improve the evaluation accuracy of the classifier. The analysis and comparison under different fold times show that the accuracy rate of classification using the CRF model is higher than the HMM. This method brings some new lights on the accurate classification of the bearing faults.

HMM_Model-Checker pour la vérification probabiliste HMM_Model ...

African Journals Online (AJOL)

ASSIA

probabiliste –Télescope Hubble. Abstract. Probabilistic verification for embedded systems continues to attract more and more followers in the research community. Given a probabilistic model, a formula of temporal logic, describing a property of a system and an exploration algorithm to check whether the property is satisfied ...

A Self-Adaptive Hidden Markov Model for Emotion Classification in Chinese Microblogs

Directory of Open Access Journals (Sweden)

Li Liu

2015-01-01

we propose a modified version of hidden Markov model (HMM classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness.

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yuqi Dong

2016-12-01

Full Text Available Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

Two-Stage Hidden Markov Model in Gesture Recognition for Human Robot Interaction

Directory of Open Access Journals (Sweden)

Nhan Nguyen-Duc-Thanh

2012-07-01

Full Text Available Hidden Markov Model (HMM is very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications including gesture representation. Most research in this field, however, uses only HMM for recognizing simple gestures, while HMM can definitely be applied for whole gesture meaning recognition. This is very effectively applicable in Human-Robot Interaction (HRI. In this paper, we introduce an approach for HRI in which not only the human can naturally control the robot by hand gesture, but also the robot can recognize what kind of task it is executing. The main idea behind this method is the 2-stages Hidden Markov Model. The 1st HMM is to recognize the prime command-like gestures. Based on the sequence of prime gestures that are recognized from the 1st stage and which represent the whole action, the 2nd HMM plays a role in task recognition. Another contribution of this paper is that we use the output Mixed Gaussian distribution in HMM to improve the recognition rate. In the experiment, we also complete a comparison of the different number of hidden states and mixture components to obtain the optimal one, and compare to other methods to evaluate this performance.

An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction

Directory of Open Access Journals (Sweden)

Nguyet Nguyen

2017-11-01

Full Text Available Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM, to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on their historical data. We first use the Akaike information criterion (AIC and Bayesian information criterion (BIC to choose the numbers of states from HMM. We then use the models to predict close prices of these three stocks using both single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks. The criteria tests’ results showed that HMM with two states worked the best among two, three and four states for the three stocks. Our results also demonstrate that the HMM outperformed the naïve method in forecasting stock prices. The results also showed that active traders using HMM got a higher return than using the naïve forecast for Facebook and Google stocks. The stock price prediction method has a significant impact on stock trading and derivative hedging.

Load-based approaches for modelling visual clarity in streams at regional scale.

Science.gov (United States)

Elliott, A H; Davies-Colley, R J; Parshotam, A; Ballantine, D

2013-01-01

Reduction of visual clarity in streams by diffuse sources of fine sediment is a cause of water quality impairment in New Zealand and internationally. In this paper we introduce the concept of a load of optical cross section (LOCS), which can be used for load-based management of light-attenuating substances and for water quality models that are based on mass accounting. In this approach, the beam attenuation coefficient (units of m(-1)) is estimated from the inverse of the visual clarity (units of m) measured with a black disc. This beam attenuation coefficient can also be considered as an optical cross section (OCS) per volume of water, analogous to a concentration. The instantaneous 'flux' of cross section is obtained from the attenuation coefficient multiplied by the water discharge, and this can be accumulated over time to give an accumulated 'load' of cross section (LOCS). Moreover, OCS is a conservative quantity, in the sense that the OCS of two combined water volumes is the sum of the OCS of the individual water volumes (barring effects such as coagulation, settling, or sorption). The LOCS can be calculated for a water quality station using rating curve methods applied to measured time series of visual clarity and flow. This approach was applied to the sites in New Zealand's National Rivers Water Quality Network (NRWQN). Although the attenuation coefficient follows roughly a power relation with flow at some sites, more flexible loess rating curves are required at other sites. The hybrid mechanistic-statistical catchment model SPARROW (SPAtially Referenced Regressions On Watershed attributes), which is based on a mass balance for mean annual load, was then applied to the NRWQN dataset. Preliminary results from this model are presented, highlighting the importance of factors related to erosion, such as rainfall, slope, hardness of catchment rock types, and the influence of pastoral development on the load of optical cross section.

A Framework for Bioacoustic Vocalization Analysis Using Hidden Markov Models

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Ebenezer Out-Nyarko

2009-11-01

Full Text Available Using Hidden Markov Models (HMMs as a recognition framework for automatic classification of animal vocalizations has a number of benefits, including the ability to handle duration variability through nonlinear time alignment, the ability to incorporate complex language or recognition constraints, and easy extendibility to continuous recognition and detection domains. In this work, we apply HMMs to several different species and bioacoustic tasks using generalized spectral features that can be easily adjusted across species and HMM network topologies suited to each task. This experimental work includes a simple call type classification task using one HMM per vocalization for repertoire analysis of Asian elephants, a language-constrained song recognition task using syllable models as base units for ortolan bunting vocalizations, and a stress stimulus differentiation task in poultry vocalizations using a non-sequential model via a one-state HMM with Gaussian mixtures. Results show strong performance across all tasks and illustrate the flexibility of the HMM framework for a variety of species, vocalization types, and analysis tasks.

Modeling of Generic Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; Bendtsen, Jan Dimon; la Cour-Harbo, Anders

2009-01-01

This paper presents the result of the modelling and verification of a generic slung load system using a small-scale helicopter. The model is intended for use in simulation, pilot training, estimation, and control. The model is derived using a redundant coordinate formulation based on Gauss...... slackening and tightening as well as aerodynamic coupling between the helicopter and the load. Furthermore, it is shown how the model can be easily used for multi-lift systems either with multiple helicopters or multiple loads. A numerical stabilisation algorithm is introduced and finally the use...... of the model is illustrated through simulations and flight verifications.  ...

Mathematical Modeling of Column-Base Connections under Monotonic Loading

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Gholamreza Abdollahzadeh

2014-12-01

Full Text Available Some considerable damage to steel structures during the Hyogo-ken Nanbu Earthquake occurred. Among them, many exposed-type column bases failed in several consistent patterns, such as brittle base plate fracture, excessive bolt elongation, unexpected early bolt failure, and inferior construction work, etc. The lessons from these phenomena led to the need for improved understanding of column base behavior. Joint behavior must be modeled when analyzing semi-rigid frames, which is associated with a mathematical model of the moment–rotation curve. The most accurate model uses continuous nonlinear functions. This article presents three areas of steel joint research: (1 analysis methods of semi-rigid joints; (2 prediction methods for the mechanical behavior of joints; (3 mathematical representations of the moment–rotation curve. In the current study, a new exponential model to depict the moment–rotation relationship of column base connection is proposed. The proposed nonlinear model represents an approach to the prediction of M–θ curves, taking into account the possible failure modes and the deformation characteristics of the connection elements. The new model has three physical parameters, along with two curve-fitted factors. These physical parameters are generated from dimensional details of the connection, as well as the material properties. The M–θ curves obtained by the model are compared with published connection tests and 3D FEM research. The proposed mathematical model adequately comes close to characterizing M–θ behavior through the full range of loading/rotations. As a result, modeling of column base connections using the proposed mathematical model can give crucial beforehand information, and overcome the disadvantages of time consuming workmanship and cost of experimental studies.

A Constraint Model for Constrained Hidden Markov Models

DEFF Research Database (Denmark)

Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp

2009-01-01

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we extend HMMs with constraints and show how the familiar Viterbi algorithm can be generalized, based on constraint solving ...

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

International Nuclear Information System (INIS)

Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

2008-01-01

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

Estimation of the Diesel Particulate Filter Soot Load Based on an Equivalent Circuit Model

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Yanting Du

2018-02-01

Full Text Available In order to estimate the diesel particulate filter (DPF soot load and improve the accuracy of regeneration timing, a novel method based on an equivalent circuit model is proposed based on the electric-fluid analogy. This proposed method can reduce the impact of the engine transient operation on the soot load, accurately calculate the flow resistance, and improve the estimation accuracy of the soot load. Firstly, the least square method is used to identify the flow resistance based on the World Harmonized Transient Cycle (WHTC test data, and the relationship between flow resistance, exhaust temperature and soot load is established. Secondly, the online estimation of the soot load is achieved by using the dual extended Kalman filter (DEKF. The results show that this method has good convergence and robustness with the maximal absolute error of 0.2 g/L at regeneration timing, which can meet engineering requirements. Additionally, this method can estimate the soot load under engine transient operating conditions and avoids a large number of experimental tests, extensive calibration and the analysis of complex chemical reactions required in traditional methods.

Electrical load modeling

Energy Technology Data Exchange (ETDEWEB)

Valgas, Helio Moreira; Pinto, Roberto del Giudice R.; Franca, Carlos [Companhia Energetica de Minas Gerais (CEMIG), Belo Horizonte, MG (Brazil); Lambert-Torres, Germano; Silva, Alexandre P. Alves da; Pires, Robson Celso; Costa, Junior, Roberto Affonso [Escola Federal de Engenharia de Itajuba, MG (Brazil)

1994-12-31

Accurate dynamic load models allow more precise calculations of power system controls and stability limits, which are critical mainly in the operation planning of power systems. This paper describes the development of a computer program (software) for static and dynamic load model studies using the measurement approach for the CEMIG system. Two dynamic load model structures are developed and tested. A procedure for applying a set of measured data from an on-line transient recording system to develop load models is described. (author) 6 refs., 17 figs.

Pairagon+N-SCAN_EST: a model-based gene annotation pipeline

DEFF Research Database (Denmark)

Arumugam, Manimozhiyan; Wei, Chaochun; Brown, Randall H

2006-01-01

This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci...... with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST...

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

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Surovcik Katharina

2006-03-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired

Reliability-Based Calibration of Load Duration Factors for Timber Structures

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Svensson, Staffan; Stang, Birgitte Friis Dela

2005-01-01

John Dalsgaard Sørensen, Staffan Svensson, Birgitte Dela Stang : Reliability-Based Calibration of Load Duration Factors for Timber Structures     Abstract :   The load bearing capacity of timber structures decrease with time depending on the type of load and timber. Based on representative limit...... states and stochastic models for timber structures, load duration factors are calibrated using probabilistic methods. Load duration e.ects are estimated on basis of simulation of realizations of wind, snow and imposed loads in accordance with the load models in the Danish structural codes. Three damage...... accumulation models are considered, namely Gerhards model, Barrett and Foschi _ s model and Foschi and Yao _ s model. The parameters in these models are .tted by the Maximum Likelihood Method using data relevant for Danish structural timber and the statistical uncertainty is quanti .ed. The reliability...

SVM-dependent pairwise HMM: an application to protein pairwise alignments.

Science.gov (United States)

Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F

2017-12-15

Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Power Load Prediction Based on Fractal Theory

OpenAIRE

Jian-Kai, Liang; Cattani, Carlo; Wan-Qing, Song

2015-01-01

The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and...

Hierarchical material models for fragmentation modeling in NIF-ALE-AMR

International Nuclear Information System (INIS)

Fisher, A C; Masters, N D; Koniges, A E; Anderson, R W; Gunney, B T N; Wang, P; Becker, R; Dixit, P; Benson, D J

2008-01-01

Fragmentation is a fundamental process that naturally spans micro to macroscopic scales. Recent advances in algorithms, computer simulations, and hardware enable us to connect the continuum to microstructural regimes in a real simulation through a heterogeneous multiscale mathematical model. We apply this model to the problem of predicting how targets in the NIF chamber dismantle, so that optics and diagnostics can be protected from damage. The mechanics of the initial material fracture depend on the microscopic grain structure. In order to effectively simulate the fragmentation, this process must be modeled at the subgrain level with computationally expensive crystal plasticity models. However, there are not enough computational resources to model the entire NIF target at this microscopic scale. In order to accomplish these calculations, a hierarchical material model (HMM) is being developed. The HMM will allow fine-scale modeling of the initial fragmentation using computationally expensive crystal plasticity, while the elements at the mesoscale can use polycrystal models, and the macroscopic elements use analytical flow stress models. The HMM framework is built upon an adaptive mesh refinement (AMR) capability. We present progress in implementing the HMM in the NIF-ALE-AMR code. Additionally, we present test simulations relevant to NIF targets

Hierarchical material models for fragmentation modeling in NIF-ALE-AMR

Energy Technology Data Exchange (ETDEWEB)

Fisher, A C; Masters, N D; Koniges, A E; Anderson, R W; Gunney, B T N; Wang, P; Becker, R [Lawrence Livermore National Laboratory, PO Box 808, Livermore, CA 94551 (United States); Dixit, P; Benson, D J [University of California San Diego, 9500 Gilman Dr., La Jolla. CA 92093 (United States)], E-mail: fisher47@llnl.gov

2008-05-15

Fragmentation is a fundamental process that naturally spans micro to macroscopic scales. Recent advances in algorithms, computer simulations, and hardware enable us to connect the continuum to microstructural regimes in a real simulation through a heterogeneous multiscale mathematical model. We apply this model to the problem of predicting how targets in the NIF chamber dismantle, so that optics and diagnostics can be protected from damage. The mechanics of the initial material fracture depend on the microscopic grain structure. In order to effectively simulate the fragmentation, this process must be modeled at the subgrain level with computationally expensive crystal plasticity models. However, there are not enough computational resources to model the entire NIF target at this microscopic scale. In order to accomplish these calculations, a hierarchical material model (HMM) is being developed. The HMM will allow fine-scale modeling of the initial fragmentation using computationally expensive crystal plasticity, while the elements at the mesoscale can use polycrystal models, and the macroscopic elements use analytical flow stress models. The HMM framework is built upon an adaptive mesh refinement (AMR) capability. We present progress in implementing the HMM in the NIF-ALE-AMR code. Additionally, we present test simulations relevant to NIF targets.

Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

Directory of Open Access Journals (Sweden)

Huiru Zhao

2015-03-01

Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

Nonlinear behaviour of cantilevered carbon nanotube resonators based on a new nonlinear electrostatic load model

Science.gov (United States)

Farokhi, Hamed; Païdoussis, Michael P.; Misra, Arun K.

2018-04-01

The present study examines the nonlinear behaviour of a cantilevered carbon nanotube (CNT) resonator and its mass detection sensitivity, employing a new nonlinear electrostatic load model. More specifically, a 3D finite element model is developed in order to obtain the electrostatic load distribution on cantilevered CNT resonators. A new nonlinear electrostatic load model is then proposed accounting for the end effects due to finite length. Additionally, a new nonlinear size-dependent continuum model is developed for the cantilevered CNT resonator, employing the modified couple stress theory (to account for size-effects) together with the Kelvin-Voigt model (to account for nonlinear damping); the size-dependent model takes into account all sources of nonlinearity, i.e. geometrical and inertial nonlinearities as well as nonlinearities associated with damping, small-scale, and electrostatic load. The nonlinear equation of motion of the cantilevered CNT resonator is obtained based on the new models developed for the CNT resonator and the electrostatic load. The Galerkin method is then applied to the nonlinear equation of motion, resulting in a set of nonlinear ordinary differential equations, consisting of geometrical, inertial, electrical, damping, and size-dependent nonlinear terms. This high-dimensional nonlinear discretized model is solved numerically utilizing the pseudo-arclength continuation technique. The nonlinear static and dynamic responses of the system are examined for various cases, investigating the effect of DC and AC voltages, length-scale parameter, nonlinear damping, and electrostatic load. Moreover, the mass detection sensitivity of the system is examined for possible application of the CNT resonator as a nanosensor.

Model-based active control of a continuous structure subjected to moving loads

Science.gov (United States)

Stancioiu, D.; Ouyang, H.

2016-09-01

Modelling of a structure is an important preliminary step of structural control. The main objectives of the modelling, which are almost always antagonistic are accuracy and simplicity of the model. The first part of this study focuses on the experimental and theoretical modelling of a structure subjected to the action of one or two decelerating moving carriages modelled as masses. The aim of this part is to obtain a simple but accurate model which will include not only the structure-moving load interaction but also the actuators dynamics. A small scale rig is designed to represent a four-span continuous metallic bridge structure with miniature guiding rails. A series of tests are run subjecting the structure to the action of one or two minicarriages with different loads that were launched along the structure at different initial speeds. The second part is dedicated to model based control design where a feedback controller is designed and tested against the validated model. The study shows that a positive position feedback is able to improve system dynamics but also shows some of the limitations of state- space methods for this type of system.

Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

Science.gov (United States)

Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao

2014-11-01

A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television

Thrust and torque characteristics based on a new cutter-head load model

Science.gov (United States)

Liu, Jianqin; Ren, Jiabao; Guo, Wei

2015-07-01

Full face rock tunnel boring machine(TBM) has been widely used in hard rock tunnels, however, there are few published theory about cutter-head design, and the design criteria of cutter-head under complex geological is not clear yet. To deal with the complex relationship among geological parameters, cutter parameters, and operating parameters during tunneling processes, a cutter-head load model is established by using CSM(Colorado school of mines) prediction model. Force distribution on cutter-head under a certain geology is calculated with the new established load model, and result shows that inner cutters bear more force than outer cutters, combining with disc cutters abrasion; a general principle of disc cutters' layout design is proposed. Within the model, the relationship among rock uniaxial compressive strength(UCS), penetration and thrust on cutter-head are analyzed, and the results shows that with increasing penetration, cutter thrust increases, but the growth rate slows and higher penetration makes lower special energy(SE). Finally, a fitting mathematical model of ZT(ratio of cutter-head torque and thrust) and penetration is established, and verified by TB880E, which can be used to direct how to set thrust and torque on cutter-head. When penetration is small, the cutter-head thrust is the main limiting factor in tunneling; when the penetration is large, cutter-head torque is the major limiting factor in tunneling. Based on the new cutter-head load model, thrust and torque characteristics of TBM further are researched and a new way for cutter-head layout design and TBM tunneling operations is proposed.

Improved hidden Markov model for nosocomial infections.

Science.gov (United States)

Khader, Karim; Leecaster, Molly; Greene, Tom; Samore, Matthew; Thomas, Alun

2014-12-01

We propose a novel hidden Markov model (HMM) for parameter estimation in hospital transmission models, and show that commonly made simplifying assumptions can lead to severe model misspecification and poor parameter estimates. A standard HMM that embodies two commonly made simplifying assumptions, namely a fixed patient count and binomially distributed detections is compared with a new alternative HMM that does not require these simplifying assumptions. Using simulated data, we demonstrate how each of the simplifying assumptions used by the standard model leads to model misspecification, whereas the alternative model results in accurate parameter estimates. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

A first approach to Arrhythmogenic Cardiomyopathy detection through ECG and Hidden Markov Models

Energy Technology Data Exchange (ETDEWEB)

Jimenez-Serrano, S.; Sanz Sanchez, J.; Martínez Hinarejos, C.D.; Igual Muñoz, B.; Millet Roig, J.; Zorio Grima, Z.; Castells, F.

2016-07-01

Arrhythmogenic Cardiomyopathy (ACM) is a heritable cardiac disease causing sudden cardiac death in young people. Its clinical diagnosis includes major and minor criteria based on alterations of the electrocardiogram (ECG). The aim of this study is to evaluate Hidden Markov Models (HMM) in order to assess its possible potential of classification among subjects affected by ACM and those relatives who do not suffer the disease through 12-lead ECG recordings. Database consists of 12-lead ECG recordings from 32 patients diagnosed with ACM, and 37 relatives of those affected, but without gene mutation. Using the HTK toolkit and a hold-out strategy in order to train and evaluate a set of HMM models, we performed a grid search through the number of states and Gaussians across these HMM models. Results show that two different HMM models achieved the best balance between sensibility and specificity. The first one needed 35 states and 2 Gaussians and its performance was 0.7 and 0.8 in sensibility and specificity respectively. The second one achieved a sensibility and specificity values of 0.8 and 0.7 respectively with 50 states and 4 Gaussians. The results of this study show that HMM models can achieve an acceptable level of sensibility and specificity in the classification among ECG registers between those affected by ACM and the control group. All the above suggest that this approach could help to detect the disease in a non-invasive way, especially within the context of family screening, improving sensitivity in detection by ECG. (Author)

A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

Directory of Open Access Journals (Sweden)

Ashfaq Ahmad

2015-12-01

Full Text Available In the operation of a smart grid (SG, day-ahead load forecasting (DLF is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN, predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN-based model for SGs is able to capture the non-linearity(ies in the history load curve with 97 . 11 % accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38 . 50 % .

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Science.gov (United States)

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

Directory of Open Access Journals (Sweden)

Da Liu

2013-01-01

Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

Finite element-based limit load of piping branch junctions under combined loadings

International Nuclear Information System (INIS)

Xuan Fuzhen; Li Peining

2004-01-01

The limit load is an important input parameter in engineering defect-assessment procedures and strength design. In the present work, a total of 100 different piping branch junction models for the limit load calculation were performed under combined internal pressure and moments in use of non-linear finite element (FE) method. Three different existing accumulation rules for limit load, i.e., linear equation, parabolic equation and quadratic equation were discussed on the basis of FE results. A novel limit load solution was developed based on detailed three-dimensional FE limit analyses which accommodated the geometrical parameter influence, together with analytical solutions based on equilibrium stress fields. Finally, six experimental results were provided to justify the presented equation. According to the FE limit analysis, limit load interaction of the piping tees under combined pressure and moments has a relationship with the geometrical parameters, especially with the diameter ratio d/D. The predicted limit loads from the presented formula are very close to the experimental data. The resulting limit load solution is given in a closed form, and thus can be easily used in practice

Post processing of optically recognized text via second order hidden Markov model

Science.gov (United States)

Poudel, Srijana

In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.

Optimum load distribution between heat sources based on the Cournot model

Science.gov (United States)

Penkovskii, A. V.; Stennikov, V. A.; Khamisov, O. V.

2015-08-01

One of the widespread models of the heat supply of consumers, which is represented in the "Single buyer" format, is considered. The methodological base proposed for its description and investigation presents the use of principles of the theory of games, basic propositions of microeconomics, and models and methods of the theory of hydraulic circuits. The original mathematical model of the heat supply system operating under conditions of the "Single buyer" organizational structure provides the derivation of a solution satisfying the market Nash equilibrium. The distinctive feature of the developed mathematical model is that, along with problems solved traditionally within the bounds of bilateral relations of heat energy sources-heat consumer, it considers a network component with its inherent physicotechnical properties of the heat network and business factors connected with costs of the production and transportation of heat energy. This approach gives the possibility to determine optimum levels of load of heat energy sources. These levels provide the given heat energy demand of consumers subject to the maximum profit earning of heat energy sources and the fulfillment of conditions for formation of minimum heat network costs for a specified time. The practical realization of the search of market equilibrium is considered by the example of a heat supply system with two heat energy sources operating on integrated heat networks. The mathematical approach to the solution search is represented in the graphical form and illustrates computations based on the stepwise iteration procedure for optimization of levels of loading of heat energy sources (groping procedure by Cournot) with the corresponding computation of the heat energy price for consumers.

Modeling of plug-in electric vehicle travel patterns and charging load based on trip chain generation

Science.gov (United States)

Wang, Dai; Gao, Junyu; Li, Pan; Wang, Bin; Zhang, Cong; Saxena, Samveg

2017-08-01

Modeling PEV travel and charging behavior is the key to estimate the charging demand and further explore the potential of providing grid services. This paper presents a stochastic simulation methodology to generate itineraries and charging load profiles for a population of PEVs based on real-world vehicle driving data. In order to describe the sequence of daily travel activities, we use the trip chain model which contains the detailed information of each trip, namely start time, end time, trip distance, start location and end location. A trip chain generation method is developed based on the Naive Bayes model to generate a large number of trips which are temporally and spatially coupled. We apply the proposed methodology to investigate the multi-location charging loads in three different scenarios. Simulation results show that home charging can meet the energy demand of the majority of PEVs in an average condition. In addition, we calculate the lower bound of charging load peak on the premise of lowest charging cost. The results are instructive for the design and construction of charging facilities to avoid excessive infrastructure.

Life-expetancy formulas based on the load model

International Nuclear Information System (INIS)

Gubin, A.T.

1994-01-01

Reduction in life expectancy is already used as a supplementary measure of radiation safety and has been widely investigated in radiological experiments. Usually, the median or the mean life expetancy calculated form the onset of radiation exposure has been considered. Considerable data on the dependence of the reduction in median and mean life expectancy on the dose in different exposure conditions has now been accumulated. The development of calculation methods for different exposure conditions would facilitate the generalization of these and other data characterizing the change in lifetime under the action of ionizing radiation and their wider use in estimating radiation safety. A mathematical model of cohort extinction in normal conditions and in unfavorable circumstances (henceforward called the load model of mortality) that is in satisfactory agreement with observational data has been previously proposed. The aim of the present work is to obtain expressions for the median and mean life expectancy on the basis of this model. Formulas for the radiational load and age at the onset of irradiation and their relationship are derived for low levels of radiation. Particular attention is paid to short- and long-term radiation exposure

Preview-based Asymmetric Load Reduction of Wind Turbines

DEFF Research Database (Denmark)

Madsen, Mathias; Filsø, Jakob; Soltani, Mohsen

2012-01-01

Controller (MPC) developed is based on a model with individual blade pitching to utilize the LIDAR measurements. The MPC must also maintain a given power reference while satisfying a set of actuator constraints. The designed controller was tested on a 5 MW wind turbine in the FAST simulator and compared......Fatigue loads on wind turbines caused by an asymmetric wind field become an increasing concern when the scale of wind turbines increases. This paper presents a model based predictive approach to reduce asymmetric loads by using Light Detection And Ranging (LIDAR) measurements. The Model Predictive...

Least square regression based integrated multi-parameteric demand modeling for short term load forecasting

International Nuclear Information System (INIS)

Halepoto, I.A.; Uqaili, M.A.

2014-01-01

Nowadays, due to power crisis, electricity demand forecasting is deemed an important area for socioeconomic development and proper anticipation of the load forecasting is considered essential step towards efficient power system operation, scheduling and planning. In this paper, we present STLF (Short Term Load Forecasting) using multiple regression techniques (i.e. linear, multiple linear, quadratic and exponential) by considering hour by hour load model based on specific targeted day approach with temperature variant parameter. The proposed work forecasts the future load demand correlation with linear and non-linear parameters (i.e. considering temperature in our case) through different regression approaches. The overall load forecasting error is 2.98% which is very much acceptable. From proposed regression techniques, Quadratic Regression technique performs better compared to than other techniques because it can optimally fit broad range of functions and data sets. The work proposed in this paper, will pave a path to effectively forecast the specific day load with multiple variance factors in a way that optimal accuracy can be maintained. (author)

An exact model for airline flight network optimization based on transport momentum and aircraft load factor

Directory of Open Access Journals (Sweden)

Daniel Jorge Caetano

2017-12-01

Full Text Available The problem of airline flight network optimization can be split into subproblems such as Schedule Generation (SG and Fleet Assignment (FA, solved in consecutive steps or in an integrated way, usually based on monetary costs and revenue forecasts. A linear pro­gramming model to solve SG and FA in an integrated way is presented, but with an al­ternative approach based on transport momentum and aircraft load factor. This alterna­tive approach relies on demand forecast and allows obtaining solutions considering min­imum average load factors. Results of the proposed model applications to instances of a regional Brazilian airline are presented. The comparison of the schedules generated by the proposed approach against those obtained by applying a model based on mone­tary costs and revenue forecasts demonstrates the validity of this alternative approach for airlines network planning.

Probabilistic Load Models for Simulating the Impact of Load Management

DEFF Research Database (Denmark)

Chen, Peiyuan; Bak-Jensen, Birgitte; Chen, Zhe

2009-01-01

. It is concluded that the AR(12) model is favored with limited measurement data and that the joint-normal model may provide better results with a large data set. Both models can be applied in general to model load time series and used in time-sequential simulation of distribution system planning.......This paper analyzes a distribution system load time series through autocorrelation coefficient, power spectral density, probabilistic distribution and quantile value. Two probabilistic load models, i.e. the joint-normal model and the autoregressive model of order 12 (AR(12)), are proposed...... to simulate the impact of load management. The joint-normal model is superior in modeling the tail region of the hourly load distribution and implementing the change of hourly standard deviation. Whereas the AR(12) model requires much less parameter and is superior in modeling the autocorrelation...

Microstructure-based constitutive modeling of TRIP steel: Prediction of ductility and failure modes under different loading conditions

International Nuclear Information System (INIS)

Choi, K.S.; Liu, W.N.; Sun, X.; Khaleel, M.A.

2009-01-01

We study the ultimate ductility and failure modes of a commercial transformation-induced plasticity (TRIP) 800 steel under different loading conditions with an advanced microstructure-based finite-element analysis. The representative volume element (RVE) for the TRIP 800 under examination is developed based on an actual microstructure obtained from scanning electron microscopy. The ductile failure of the TRIP 800 under different loading conditions is predicted in the form of plastic strain localization without any prescribed failure criteria for the individual phases. This indicates that the microstructure-level inhomogeneity of the various constituent phases can be the key factor influencing the final ductility of the TRIP 800 steel under different loading conditions. Comparisons of the computational results with experimental measurements suggest that the microstructure-based modeling approach accurately captures the overall macroscopic behavior of the TRIP 800 steel under different loading and boundary conditions.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Directory of Open Access Journals (Sweden)

Ahmad Tamimi

Full Text Available Profile Hidden Markov Model (Profile-HMM is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Modeling river total bed material load discharge using artificial intelligence approaches (based on conceptual inputs)

Science.gov (United States)

Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

2014-06-01

This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

The Use of Hidden Markov Models for Anomaly Detection in Nuclear Core Condition Monitoring

Science.gov (United States)

Stephen, Bruce; West, Graeme M.; Galloway, Stuart; McArthur, Stephen D. J.; McDonald, James R.; Towle, Dave

2009-04-01

Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data.

Characterization of non-linear household loads for frequency domain modeling

Directory of Open Access Journals (Sweden)

Miguel Fernando Romero

2015-06-01

Full Text Available Component-based harmonic studies in public Low Voltage grids require realistic models of individual loads as well as their typical penetration ratios. As fundamental basis for the development of comprehensive models for residential users, this paper identifies the most commonly used household loads in Colombia. The loads are classified according to their Power Factor Correction (PFC circuit topology in no-PFC, passive-PFC and active-PFC devices, and a comprehensive set of loads is selected. Their behavior in terms of harmonic emission is characterized by intensive lab measurements with systematically varied supply voltage distortion. Based on several indices, the suitability of different frequency-domain modeling approaches (e.g. constant current source, decoupled and coupled Norton models is assessed.

Probabilistic model for multi-axial load combinations for wind turbines

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov

2016-01-01

into a periodic part and a perturbation term, where each part has a known probability distribution. The proposed model shows good agreement with simulated data under stationary conditions, and a design load envelope based on this model is comparable to the load envelope estimated using the standard procedure...... for determining contemporaneous loads. Using examples with simulated loads on a 10 MW wind turbine,the behavior of the bending moments acting on a blade section is illustrated under different conditions.The loading direction most critical for material failure is determined using a finite-element model...

Determination of aggregated load power consumption, under non-sinusoidal supply using an improved load model

International Nuclear Information System (INIS)

Bagheri, R.; Moghani, J.S.; Gharehpetian, G.B.; Mirtalaei, S.M.M.

2009-01-01

The harmonic content of supply voltage results in additional power losses and hence increases the load power consumption. The role of the power quality equipments on the power consumption without using an accurate model cannot be determined, too. In this paper, an improved model for aggregated loads proposed, which estimates the effects of voltage harmonics on the power consumption. The distinguished aspect of the proposed model is its parameters identification method which is based on the practical techniques, such as employing a capacitor bank or varying dummy loads in steps. The proposed model has been verified by the comparison of measured and simulated results.

Experimental and modelling buckling of wood-based columns under repeated loading

Directory of Open Access Journals (Sweden)

Nafa Z.

2010-06-01

Full Text Available Collapse of timber constructions can appear under the effect of load that exceeds the resistance of a carrying element or under the effect of a geometrical instability like buckling. In addition, loading can be constant or varying for example loads due to wind or earthquakes. The aim of this paper is to study the behaviour and the lifetime of columns in wood or based-wood material such as glulam (GL or laminated veneer lumber (LVL under repeated loading leading to buckling.

Modeling of quasi-static thrust load of wind turbines based on 1 s SCADA data

Directory of Open Access Journals (Sweden)

N. Noppe

2018-03-01

Full Text Available A reliable load history is crucial for a fatigue assessment of wind turbines. However, installing strain sensors on every wind turbine is not economically feasible. In this paper, a technique is proposed to reconstruct the thrust load history of a wind turbine based on high-frequency Supervisory Control and Data Acquisition (SCADA data. Strain measurements recorded during a short period of time are used to train a neural network. The selection of appropriate input parameters is performed based on Pearson correlation and mutual information. Once the training is done, the model can be used to predict the thrust load based on SCADA data only. The technique is validated on two different datasets, one consisting of simulation data (using the software FAST v8, created by Jonkman and Jonkman, 2016 obtained in a controllable environment and one consisting of measurements taken at an offshore wind turbine. In general, the relative error between simulated or measured and predicted thrust load barely exceeds 15 % during normal operation.

Reducing Turbine Mechanical Loads Using Flow Model-Based Wind Farm Controller

DEFF Research Database (Denmark)

Kazda, Jonas; Cutululis, Nicolaos Antonio

Cumulated O&M costs of offshore wind farms are comparable with wind turbine CAPEX of such wind farm. In wind farms, wake effects can result in up to 80% higher fatigue loads at downstream wind turbines [1] and consequently larger O&M costs. The present work therefore investigates to reduce...... these loads during the provision of grid balancing services using optimal model-based wind farm control. Wind farm controllers coordinate the operating point of wind turbines in a wind farm in order to achieve a given objective. The investigated objective of the control in this work is to follow a total wind...... farm power reference while reducing the tower bending moments of the turbines in the wind farm. The wind farm controller is tested on a 8 turbine array, which is representative of a typical offshore wind farm. The operation of the wind farm is simulated using the dynamic wind farm simulation tool S imWind...

Short-term Power Load Forecasting Based on Balanced KNN

Science.gov (United States)

Lv, Xianlong; Cheng, Xingong; YanShuang; Tang, Yan-mei

2018-03-01

To improve the accuracy of load forecasting, a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics, the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes, the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing, the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data. The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster, and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.

Multivariate Modelling of Extreme Load Combinations for Wind Turbines

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov

2015-01-01

into a periodic part and a perturbation term, where each part has a known probability distribution. The proposed model shows good agreement with simulated data under stationary conditions, and a design load envelope based on this model is comparable to the load envelope estimated using the standard procedure...

Using hidden Markov models to align multiple sequences.

Science.gov (United States)

Mount, David W

2009-07-01

A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.

Load modeling for sharp V-cutter cutting litchi ( Litchi chinensis Sonn ...

African Journals Online (AJOL)

harvesting. Cutting load is a key parameter for 'hand-held auto-picker' operation. However, there is still no suitable model for cutting load setting. Hence, a model describing the relationship among cutting load, blade angle and friction coefficient was developed for cutting operation by sharp V-cutters. The model was based ...

An electricity price model with consideration to load and gas price effects.

Science.gov (United States)

Huang, Min-xiang; Tao, Xiao-hu; Han, Zhen-xiang

2003-01-01

Some characteristics of the electricity load and prices are studied, and the relationship between electricity prices and gas (fuel) prices is analyzed in this paper. Because electricity prices are strongly dependent on load and gas prices, the authors constructed a model for electricity prices based on the effects of these two factors; and used the Geometric Mean Reversion Brownian Motion (GMRBM) model to describe the electricity load process, and a Geometric Brownian Motion(GBM) model to describe the gas prices; deduced the price stochastic process model based on the above load model and gas price model. This paper also presents methods for parameters estimation, and proposes some methods to solve the model.

MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

Directory of Open Access Journals (Sweden)

Yuteng Xiao

2017-01-01

Full Text Available Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response. However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

ANALYTIC WORD RECOGNITION WITHOUT SEGMENTATION BASED ON MARKOV RANDOM FIELDS

NARCIS (Netherlands)

Coisy, C.; Belaid, A.

2004-01-01

In this paper, a method for analytic handwritten word recognition based on causal Markov random fields is described. The words models are HMMs where each state corresponds to a letter; each letter is modelled by a NSHP­HMM (Markov field). Global models are build dynamically, and used for recognition

Name segmentation using hidden Markov models and its application in record linkage

Directory of Open Access Journals (Sweden)

Rita de Cassia Braga Gonçalves

2014-10-01

Full Text Available This study aimed to evaluate the use of hidden Markov models (HMM for the segmentation of person names and its influence on record linkage. A HMM was applied to the segmentation of patient’s and mother’s names in the databases of the Mortality Information System (SIM, Information Subsystem for High Complexity Procedures (APAC, and Hospital Information System (AIH. A sample of 200 patients from each database was segmented via HMM, and the results were compared to those from segmentation by the authors. The APAC-SIM and APAC-AIH databases were linked using three different segmentation strategies, one of which used HMM. Conformity of segmentation via HMM varied from 90.5% to 92.5%. The different segmentation strategies yielded similar results in the record linkage process. This study suggests that segmentation of Brazilian names via HMM is no more effective than traditional segmentation approaches in the linkage process.

Using adaptive network based fuzzy inference system to forecast regional electricity loads

International Nuclear Information System (INIS)

Ying, L.-C.; Pan, M.-C.

2008-01-01

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

2008-02-15

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

Optimisation of Hidden Markov Model using Baum–Welch algorithm ...

Indian Academy of Sciences (India)

The present work is a part of development of Hidden Markov Model. (HMM) based ... the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum ..... data collection teams of Snow and Avalanche Study.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

Science.gov (United States)

Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

2014-01-01

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

Simulation of Nitrogen and Phosphorus Load Runoff by a GIS-based Distributed Model for Chikugo River Watershed

Science.gov (United States)

Iseri, Haruka; Hiramatsu, Kazuaki; Harada, Masayoshi

A distributed model was developed in order to simulate the process of nitrogen and phosphorus load runoff in the semi-urban watershed of the Chikugo River, Japan. A grid of cells 1km in size was laid over the study area, and several input variables for each cell area including DEM, land use and statistical data were extracted by GIS. In the process of water runoff, hydrograph calculated at Chikugo Barrage was in close agreement with the observed one, which achieved Nash-Sutcliffe coefficient of 0.90. In addition, the model simulated reasonably well the movement of TN and TP at each station. The model was also used to analyze three scenarios based on the watershed management: (1) reduction of nutrient loads from livestock farm, (2) improvement of septic tanks' wastewater treatment system and (3) application of purification function of paddy fields. As a result, effectiveness of management strategy in each scenario depended on land use patterns. The reduction rates of nutrient load effluent in scenarios (1) and (3) were higher than that in scenario (2). The present result suggests that an appropriate management of livestock farm together with the effective use of paddy environment would have significant effects on the reduction of nutrient loads. A suitable management strategy should be planned based on the land use pattern in the watershed.

Laboratory Load Model Based on 150 kVA Power Frequency Converter and Simulink Real-Time – Concept, Implementation, Experiments

Directory of Open Access Journals (Sweden)

Robert Małkowski

2016-09-01

Full Text Available First section of the paper provides technical specification of laboratory load model basing on 150 kVA power frequency converter and Simulink Real-Time platform. Assumptions, as well as control algorithm structure is presented. Theoretical considerations based on criteria which load types may be simulated using discussed laboratory setup, are described. As described model contains transformer with thyristor-controlled tap changer, wider scope of device capabilities is presented. Paper lists and describes tunable parameters, both: tunable during device operation and changed only before starting the experiment. Implementation details are given in second section of paper. Hardware structure is presented and described. Information about used communication interface, data maintenance and storage solution, as well as used Simulink real-time features are presented. List and description of all measurements is provided. Potential of laboratory setup modifications is evaluated. Third section describes performed laboratory tests. Different load configurations are described and experimental results are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area and arbitrary active and reactive power regulation basing on defined schedule. Different operation modes of control algorithm are described: apparent power control, active and reactive power control, active and reactive current RMS value control.

An alternative method for centrifugal compressor loading factor modelling

Science.gov (United States)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Simulations of Lithium-Based Neutron Coincidence Counter for Gd-Loaded Fuel

Energy Technology Data Exchange (ETDEWEB)

Cowles, Christian C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Kouzes, Richard T. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Siciliano, Edward R. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2014-10-01

The Department of Energy Office of Nuclear Safeguards and Security (NA-241) is supporting the project Lithium-Based Alternative Neutron Detection Technology Coincidence Counting for Gd-loaded Fuels at Pacific Northwest National Laboratory for the development of a lithium-based neutron coincidence counter for nondestructively assaying Gd loaded nuclear fuel. This report provides results from MCNP simulations of a lithium-based coincidence counter for the possible measurement of Gd-loaded nuclear fuel. A comparison of lithium-based simulations and UNCL-II simulations with and without Gd loaded fuel is provided. A lithium-based model, referred to as PLNS3A-R1, showed strong promise for assaying Gd loaded fuel.

Primitive Based Action Representation and Recognition

DEFF Research Database (Denmark)

Baby, Sanmohan; Krüger, Volker

2009-01-01

a sequential and statistical     learning algorithm for   automatic detection of the action primitives and the action grammar   based on these primitives.  We model a set of actions using a   single HMM whose structure is learned incrementally as we observe   new types.   Actions are modeled with sufficient...

Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium

Directory of Open Access Journals (Sweden)

Xiao Han

2017-12-01

Full Text Available This paper focuses on the optimal intraday scheduling of a distribution system that includes renewable energy (RE generation, energy storage systems (ESSs, and thermostatically controlled loads (TCLs. This system also provides time-of-use pricing to customers. Unlike previous studies, this study attempts to examine how to optimize the allocation of electric energy and to improve the equilibrium of the load curve. Accordingly, we propose a concept of load equilibrium entropy to quantify the overall equilibrium of the load curve and reflect the allocation optimization of electric energy. Based on this entropy, we built a novel multi-objective optimal dispatching model to minimize the operational cost and maximize the load curve equilibrium. To aggregate TCLs into the optimization objective, we introduced the concept of a virtual power plant (VPP and proposed a calculation method for VPP operating characteristics based on the equivalent thermal parameter model and the state-queue control method. The Particle Swarm Optimization algorithm was employed to solve the optimization problems. The simulation results illustrated that the proposed dispatching model can achieve cost reductions of system operations, peak load curtailment, and efficiency improvements, and also verified that the load equilibrium entropy can be used as a novel index of load characteristics.

Nonlinear modelling and dynamic stability analysis of a flexible Cartesian robotic manipulator with base disturbance and terminal load

Directory of Open Access Journals (Sweden)

J. Ju

2017-07-01

Full Text Available The flexible Cartesian robotic manipulator (FCRM is coming into widespread application in industry. Because of the feeble rigidity and heavy deflection, the dynamic characteristics of the FCRM are easily influenced by external disturbances which mainly concentrate in the driving end and the load end. Thus, with the influence of driving base disturbance and terminal load considered, the motion differential equations of the FCRM under the plane motion of the base are constructed, which contain the forced and non-linear parametric excitations originated from the disturbances of base lateral and axial motion respectively. Considering the relationship between the coefficients of the motion differential equations and the mode shapes of the flexible manipulator, the analytic expressions of the mode shapes with terminal load are deduced. Then, based on multiple scales method and rectangular coordinate transformation, the average equations of the FCRM are derived to analyze the influence mechanism of base disturbance and terminal load on the system parametric vibration stability. The results show that terminal load mainly affects the node locations of mode shapes and mode frequencies of the FCRM, and the axial motion disturbance of the driving base introduces parametric excitation while the lateral motion disturbance generates forced excitation for the transverse vibration model of the FCRM. Furthermore, with the increase of the base excitation acceleration and terminal load, the parametric vibration instability region of the FCRM increases significantly. This study will be helpful for the dynamic characteristics analysis and vibration control of the FCRM.

A simplified model of dynamic interior cooling load evaluation for office buildings

International Nuclear Information System (INIS)

Ding, Yan; Zhang, Qiang; Wang, Zhaoxia; Liu, Min; He, Qing

2016-01-01

Highlights: • The core interior disturbance was determined by principle component analysis. • Influences of occupants on cooling load should be described using time series. • A simplified model was built to evaluate dynamic interior building cooling load. - Abstract: Predicted cooling load is a valuable tool for assessing the operation of air-conditioning systems. Compared with exterior cooling load, interior cooling load is more unpredictable. According to principle components analysis, occupancy was proved to be a typical factor influencing interior cooling loads in buildings. By exploring the regularity of interior disturbances in an office building, a simplified evaluation model for interior cooling load was established in this paper. The stochastic occupancy rate was represented by a Markov transition model. Equipment power, lighting power and fresh air were all related to occupancy rate based on time sequence. The superposition of different types of interior cooling loads was also considered in the evaluation model. The error between the evaluation results and measurement results was found to be lower than 10%. In reference to the cooling loads calculated by the traditional design method and area-based method in case study office rooms, the evaluated cooling loads were suitable for operation regulation.

Physics Based Model for Online Fault Detection in Autonomous Cryogenic Loading System

Science.gov (United States)

Kashani, Ali; Devine, Ekaterina Viktorovna P; Luchinsky, Dmitry Georgievich; Smelyanskiy, Vadim; Sass, Jared P.; Brown, Barbara L.; Patterson-Hine, Ann

2013-01-01

We report the progress in the development of the chilldown model for rapid cryogenic loading system developed at KSC. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The two-phase flow model of the chilldown is approximated as one-dimensional homogeneous fluid flow with no slip condition for the interphase velocity. The model is built using commercial SINDAFLUINT software. The results of numerical predictions are in good agreement with the experimental time traces. The obtained results pave the way to the application of the SINDAFLUINT model as a verification tool for the design and algorithm development required for autonomous loading operation.

A fault diagnosis system for interdependent critical infrastructures based on HMMs

International Nuclear Information System (INIS)

Ntalampiras, Stavros; Soupionis, Yannis; Giannopoulos, Georgios

2015-01-01

Modern society depends on the smooth functioning of critical infrastructures which provide services of fundamental importance, e.g. telecommunications and water supply. These infrastructures may suffer from faults/malfunctions coming e.g. from aging effects or they may even comprise targets of terrorist attacks. Prompt detection and accommodation of these situations is of paramount significance. This paper proposes a probabilistic modeling scheme for analyzing malicious events appearing in interdependent critical infrastructures. The proposed scheme is based on modeling the relationship between datastreams coming from two network nodes by means of a hidden Markov model (HMM) trained on the parameters of linear time-invariant dynamic systems which estimate the relationships existing among the specific nodes over consecutive time windows. Our study includes an energy network (IEEE 30 model bus) operated via a telecommunications infrastructure. The relationships among the elements of the network of infrastructures are represented by an HMM and the novel data is categorized according to its distance (computed in the probabilistic space) from the training ones. We considered two types of cyber-attacks (denial of service and integrity/replay) and report encouraging results in terms of false positive rate, false negative rate and detection delay. - Highlights: • An HMM-based scheme is proposed for analyzing malicious events in critical infrastructures. • We use the IEEE 30 model bus operated via an emulated ICT infrastructure. • Novel data is categorized based on its probabilistic distance from the training one. • We considered two types of cyber-attacks and report results of extensive experiments

Geolocating fish using Hidden Markov Models and Data Storage Tags

DEFF Research Database (Denmark)

Thygesen, Uffe Høgsbro; Pedersen, Martin Wæver; Madsen, Henrik

2009-01-01

Geolocation of fish based on data from archival tags typically requires a statistical analysis to reduce the effect of measurement errors. In this paper we present a novel technique for this analysis, one based on Hidden Markov Models (HMM's). We assume that the actual path of the fish is generated...... by a biased random walk. The HMM methodology produces, for each time step, the probability that the fish resides in each grid cell. Because there is no Monte Carlo step in our technique, we are able to estimate parameters within the likelihood framework. The method does not require the distribution...... of inference in state-space models of animals. The technique can be applied to geolocation based on light, on tidal patterns, or measurement of other variables that vary with space. We illustrate the method through application to a simulated data set where geolocation relies on depth data exclusively....

A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

Directory of Open Access Journals (Sweden)

Haitao Guo

2017-01-01

Full Text Available The discovery of cis-regulatory modules (CRMs is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.

A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model

Science.gov (United States)

2017-01-01

The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them. PMID:28497059

Modeling of phosphorus loads in sugarcane in a low-relief landscape using ontology-based simulation.

Science.gov (United States)

Kwon, Ho-Young; Grunwald, Sabine; Beck, Howard W; Jung, Yunchul; Daroub, Samira H; Lang, Timothy A; Morgan, Kelly T

2010-01-01

Water flow and P dynamics in a low-relief landscape manipulated by extensive canal and ditch drainage systems were modeled utilizing an ontology-based simulation model. In the model, soil water flux and processes between three soil inorganic P pools (labile, active, and stable) and organic P are represented as database objects. And user-defined relationships among objects are used to automatically generate computer code (Java) for running the simulation of discharge and P loads. Our objectives were to develop ontology-based descriptions of soil P dynamics within sugarcane- (Saccharum officinarum L.) grown farm basins of the Everglades Agricultural Area (EAA) and to calibrate and validate such processes with water quality monitoring data collected at one farm basin (1244 ha). In the calibration phase (water year [WY] 99-00), observed discharge totaled 11,114 m3 ha(-1) and dissolved P 0.23 kg P ha(-1); and in the validation phase (WY 02-03), discharge was 10,397 m3 ha(-1) and dissolved P 0.11 kg P ha(-). During WY 99-00 the root mean square error (RMSE) for monthly discharge was 188 m3 ha(-1) and for monthly dissolved P 0.0077 kg P ha(-1); whereas during WY 02-03 the RMSE for monthly discharge was 195 m3 ha(-1) and monthly dissolved P 0.0022 kg P ha(-1). These results were confirmed by Nash-Sutcliffe Coefficient of 0.69 (calibration) and 0.81 (validation) comparing measured and simulated P loads. The good model performance suggests that our model has promise to simulate P dynamics, which may be useful as a management tool to reduce P loads in other similar low-relief areas.

Cough event classification by pretrained deep neural network.

Science.gov (United States)

Liu, Jia-Ming; You, Mingyu; Wang, Zheng; Li, Guo-Zheng; Xu, Xianghuai; Qiu, Zhongmin

2015-01-01

Cough is an essential symptom in respiratory diseases. In the measurement of cough severity, an accurate and objective cough monitor is expected by respiratory disease society. This paper aims to introduce a better performed algorithm, pretrained deep neural network (DNN), to the cough classification problem, which is a key step in the cough monitor. The deep neural network models are built from two steps, pretrain and fine-tuning, followed by a Hidden Markov Model (HMM) decoder to capture tamporal information of the audio signals. By unsupervised pretraining a deep belief network, a good initialization for a deep neural network is learned. Then the fine-tuning step is a back propogation tuning the neural network so that it can predict the observation probability associated with each HMM states, where the HMM states are originally achieved by force-alignment with a Gaussian Mixture Model Hidden Markov Model (GMM-HMM) on the training samples. Three cough HMMs and one noncough HMM are employed to model coughs and noncoughs respectively. The final decision is made based on viterbi decoding algorihtm that generates the most likely HMM sequence for each sample. A sample is labeled as cough if a cough HMM is found in the sequence. The experiments were conducted on a dataset that was collected from 22 patients with respiratory diseases. Patient dependent (PD) and patient independent (PI) experimental settings were used to evaluate the models. Five criteria, sensitivity, specificity, F1, macro average and micro average are shown to depict different aspects of the models. From overall evaluation criteria, the DNN based methods are superior to traditional GMM-HMM based method on F1 and micro average with maximal 14% and 11% error reduction in PD and 7% and 10% in PI, meanwhile keep similar performances on macro average. They also surpass GMM-HMM model on specificity with maximal 14% error reduction on both PD and PI. In this paper, we tried pretrained deep neural network in

Functionalization of mesoporous silica membrane with a Schiff base fluorophore for Cu(II) ion sensing

Energy Technology Data Exchange (ETDEWEB)

Chen Xiaotong [Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku 980-8578, Sendai, Miyagi Prefecture (Japan); Department of Chemistry, Tsinghua University, Beijing 100084 (China); Yamaguchi, Akira [College of Science, Ibaraki University, Bunkyo 2-1-1, Mito, Ibaraki 310-8512 (Japan); Frontier Research Center for Applied Atomic Sciences, Ibaraki University, Tokai, Ibaraki 319-1106 (Japan); Namekawa, Manato [Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku 980-8578, Sendai, Miyagi Prefecture (Japan); Kamijo, Toshio [Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku 980-8578, Sendai, Miyagi Prefecture (Japan); Tsuruoka National College of Technology, Aza-Sawada, Tsuruoka 997-8511 (Japan); Teramae, Norio, E-mail: teramae@m.tohoku.ac.jp [Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku 980-8578, Sendai, Miyagi Prefecture (Japan); Tong, Aijun, E-mail: tongaj@mail.tsinghua.edu.cn [Department of Chemistry, Tsinghua University, Beijing 100084 (China)

2011-06-24

Graphical abstract: Highlights: > A hybrid mesoporous membrane (SB-HMM) functionalized by Schiff base fluorophores was fabricated. > SB-HMM showed strong fluorescence with aggregation-induced emission enhancement properties. > SB-HMM was applicable for the detection of Cu(II) in an aqueous solution with good reversibility and reproducibility. - Abstract: A Schiff base (SB) immobilized hybrid mesoporous silica membrane (SB-HMM) was prepared by immobilizing a Schiff base onto the pore surface of mesoporous silica (pore size = 3.1 nm) embedded in the pores of a porous anodic alumina membrane. In contrast to the non-fluorescent analogous SB molecule in homogeneous solutions, SB-HMM exhibited intense fluorescence due to emission enhancement caused by aggregation of SB groups on the pore surface. The high quantum efficiency of the surface SB groups allows SB-HMM to function as a fluorescent sensor for Cu(II) ions in an aqueous solution with good sensitivity, selectivity and reproducibility. Under the optimal conditions described, the linear ranges of fluorescence intensity for Cu(II) are 1.2-13.8 (M (R{sup 2} = 0.993) and 19.4-60 (R{sup 2} = 0.992) (M. The limit of detection for Cu(II) is 0.8 {mu}M on basis of the definition by IUPAC (C{sub LOD} = 3.3S{sub b}/m).

Validation of Simplified Load Equations Through Loads Measurement and Modeling of a Small Horizontal-Axis Wind Turbine Tower

Energy Technology Data Exchange (ETDEWEB)

Dana, Scott [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Van Dam, Jeroen J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Damiani, Rick R [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2018-04-24

As part of an ongoing effort to improve the modeling and prediction of small wind turbine dynamics, the National Renewable Energy Laboratory (NREL) tested a small horizontal-axis wind turbine in the field at the National Wind Technology Center. The test turbine was a 2.1-kW downwind machine mounted on an 18-m multi-section fiberglass composite tower. The tower was instrumented and monitored for approximately 6 months. The collected data were analyzed to assess the turbine and tower loads and further validate the simplified loads equations from the International Electrotechnical Commission (IEC) 61400-2 design standards. Field-measured loads were also compared to the output of an aeroelastic model of the turbine. In particular, we compared fatigue loads as measured in the field, predicted by the aeroelastic model, and calculated using the simplified design equations. Ultimate loads at the tower base were assessed using both the simplified design equations and the aeroelastic model output. The simplified design equations in IEC 61400-2 do not accurately model fatigue loads and a discussion about the simplified design equations is discussed.

Dynamic Regression Intervention Modeling for the Malaysian Daily Load

Directory of Open Access Journals (Sweden)

Fadhilah Abdrazak

2014-05-01

Full Text Available Malaysia is a unique country due to having both fixed and moving holidays.  These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors.  If these effects can be estimated and removed, the behavior of the series could be better viewed.  Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis.   Based on the linear transfer function method, a daily load model consists of either peak or average is developed.  The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

Science.gov (United States)

Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

2014-10-23

Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also

Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model

Energy Technology Data Exchange (ETDEWEB)

Dai, J.C. [College of Mechanical and Electrical Engineering, Central South University, Changsha (China); School of Electromechanical Engineering, Hunan University of Science and Technology, Xiangtan (China); Hu, Y.P.; Liu, D.S. [School of Electromechanical Engineering, Hunan University of Science and Technology, Xiangtan (China); Long, X. [Hara XEMC Windpower Co., Ltd., Xiangtan (China)

2011-03-15

The aerodynamic loads for MW scale horizontal-axis wind turbines are calculated and analyzed in the established coordinate systems which are used to describe the wind turbine. In this paper, the blade element momentum (BEM) theory is employed and some corrections, such as Prandtl and Buhl models, are carried out. Based on the B-L semi-empirical dynamic stall (DS) model, a new modified DS model for NACA63-4xx airfoil is adopted. Then, by combing BEM modified theory with DS model, a set of calculation method of aerodynamic loads for large scale wind turbines is proposed, in which some influence factors such as wind shear, tower, tower and blade vibration are considered. The research results show that the presented dynamic stall model is good enough for engineering purpose; the aerodynamic loads are influenced by many factors such as tower shadow, wind shear, dynamic stall, tower and blade vibration, etc, with different degree; the single blade endures periodical changing loads but the variations of the rotor shaft power caused by the total aerodynamic torque in edgewise direction are very small. The presented study approach of aerodynamic loads calculation and analysis is of the university, and helpful for thorough research of loads reduction on large scale wind turbines. (author)

Damage Model of Reinforced Concrete Members under Cyclic Loading

Science.gov (United States)

Wei, Bo Chen; Zhang, Jing Shu; Zhang, Yin Hua; Zhou, Jia Lai

2018-06-01

Based on the Kumar damage model, a new damage model for reinforced concrete members is established in this paper. According to the damage characteristics of reinforced concrete members subjected to cyclic loading, four judgment conditions for determining the rationality of damage models are put forward. An ideal damage index (D) is supposed to vary within a scale of zero (no damage) to one (collapse). D should be a monotone increasing function which tends to increase in the case of the same displacement amplitude. As for members under large displacement amplitude loading, the growth rate of D should be greater than that of D under small amplitude displacement loading. Subsequently, the Park-Ang damage model, the Niu-Ren damage model, the Lu-Wang damage model and the proposed damage model are analyzed for 30 experimental reinforced concrete members, including slabs, walls, beams and columns. The results show that current damage models do not fully matches the reasonable judgment conditions, but the proposed damage model does. Therefore, a conclusion can be drawn that the proposed damage model can be used for evaluating and predicting damage performance of RC members under cyclic loading.

Inelastic response of PCRV structure model with star-type support under horizontal loads

International Nuclear Information System (INIS)

Suzuki, T.; Yamaguchi, T.; Takeda, T.

1978-01-01

The report presents the test results of scaled models for prestressed concrete reactor vessel (PCRV) structure with star-shaped support under horizontal loads. A scale factor of 1 / 70 to a proto-type PCRV structure for large HTGR is used for both static and dynamic loading test models, while a 1 / 15 scaled model is used for static loading tests. The static behaviors such as a load-deflection envelope of the 1 / 70 model are predicted well by an inelastic analysis in consideration with appearance of concrete cracks and reinforcing bar yielding. It is also ascertained by the test results of the 1 / 15 model under static alternative loads that the same analysis procedure can be applicable to the evaluation of the elastic and inelastic behaviors of PCRV structure with support. Based on the static loading test results of both scaled models, a tri-linearized load-deflection envelope and an equivalent linearized mathematical model for hysteresis loop are assumed in a dynamic analysis. A dynamic response analysis of the 1 / 70 model subjected to earthquake-like base motion is conducted by the similar manner above-mentioned and the calculated results show a good correlation with the test results

Hidden Markov Model for Stock Selection

Directory of Open Access Journals (Sweden)

Nguyet Nguyen

2015-10-01

Full Text Available The hidden Markov model (HMM is typically used to predict the hidden regimes of observation data. Therefore, this model finds applications in many different areas, such as speech recognition systems, computational molecular biology and financial market predictions. In this paper, we use HMM for stock selection. We first use HMM to make monthly regime predictions for the four macroeconomic variables: inflation (consumer price index (CPI, industrial production index (INDPRO, stock market index (S&P 500 and market volatility (VIX. At the end of each month, we calibrate HMM’s parameters for each of these economic variables and predict its regimes for the next month. We then look back into historical data to find the time periods for which the four variables had similar regimes with the forecasted regimes. Within those similar periods, we analyze all of the S&P 500 stocks to identify which stock characteristics have been well rewarded during the time periods and assign scores and corresponding weights for each of the stock characteristics. A composite score of each stock is calculated based on the scores and weights of its features. Based on this algorithm, we choose the 50 top ranking stocks to buy. We compare the performances of the portfolio with the benchmark index, S&P 500. With an initial investment of $100 in December 1999, over 15 years, in December 2014, our portfolio had an average gain per annum of 14.9% versus 2.3% for the S&P 500.

Training mode's influences on the relationships between training-load models during basketball conditioning.

Science.gov (United States)

Scanlan, Aaron T; Wen, Neal; Tucker, Patrick S; Borges, Nattai R; Dalbo, Vincent J

2014-09-01

To compare perceptual and physiological training-load responses during various basketball training modes. Eight semiprofessional male basketball players (age 26.3 ± 6.7 y, height 188.1 ± 6.2 cm, body mass 92.0 ± 13.8 kg) were monitored across a 10-wk period in the preparatory phase of their training plan. Player session ratings of perceived exertion (sRPE) and heart-rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Pearson correlations were used to determine the relationships between the sRPE model and 2 HR-based models: the training impulse (TRIMP) and summated HR zones (SHRZ). One-way ANOVAs were used to compare training loads between training modes for each model. Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP r = .53, P training load than the TRIMP (15-65 AU) and SHRZ models (27-170 AU) transitioning between training modes. While the training-load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Comparisons suggest that the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. The practical benefits and sensitivity of the sRPE model support its use across different basketball training modes.

Development of Vehicle Model Test for Road Loading Analysis of Sedan Model

Science.gov (United States)

Mohd Nor, M. K.; Noordin, A.; Ruzali, M. F. S.; Hussen, M. H.

2016-11-01

Simple Structural Surfaces (SSS) method is offered as a means of organizing the process for rationalizing the basic vehicle body structure load paths. The application of this simplified approach is highly beneficial in the design development of modern passenger car structure especially during the conceptual stage. In Malaysia, however, there is no real physical model of SSS available to gain considerable insight and understanding into the function of each major subassembly in the whole vehicle structures. Based on this motivation, a physical model of SSS for sedan model with the corresponding model vehicle tests of bending and torsion is proposed in this work. The proposed approach is relatively easy to understand as compared to Finite Element Method (FEM). The results show that the proposed vehicle model test is capable to show that satisfactory load paths can give a sufficient structural stiffness within the vehicle structure. It is clearly observed that the global bending stiffness reduce significantly when more panels are removed from a complete SSS model. It is identified that parcel shelf is an important subassembly to sustain bending load. The results also match with the theoretical hypothesis, as the stiffness of the structure in an open section condition is shown weak when subjected to torsion load compared to bending load. The proposed approach can potentially be integrated with FEM to speed up the design process of automotive vehicle.

A Cyber Physical Model Based on a Hybrid System for Flexible Load Control in an Active Distribution Network

Directory of Open Access Journals (Sweden)

Yun Wang

2017-02-01

Full Text Available To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS is introduced to construct a CPS in Power Systems (Power CPS. The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is presented to reduce model complexity and improve calculation speed. The effectiveness of all the models and methods are demonstrated in the case study.

Decentralized model predictive based load frequency control in an interconnected power system

Energy Technology Data Exchange (ETDEWEB)

Mohamed, T.H., E-mail: tarekhie@yahoo.co [High Institute of Energy, South Valley University (Egypt); Bevrani, H., E-mail: bevrani@ieee.or [Dept. of Electrical Engineering and Computer Science, University of Kurdistan (Iran, Islamic Republic of); Hassan, A.A., E-mail: aahsn@yahoo.co [Faculty of Engineering, Dept. of Electrical Engineering, Minia University, Minia (Egypt); Hiyama, T., E-mail: hiyama@cs.kumamoto-u.ac.j [Dept. of Electrical Engineering and Computer Science, Kumamoto University, Kumamoto (Japan)

2011-02-15

This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

Decentralized model predictive based load frequency control in an interconnected power system

International Nuclear Information System (INIS)

Mohamed, T.H.; Bevrani, H.; Hassan, A.A.; Hiyama, T.

2011-01-01

This paper presents a new load frequency control (LFC) design using the model predictive control (MPC) technique in a multi-area power system. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. Each local area controller is designed independently such that stability of the overall closed-loop system is guaranteed. A frequency response model of multi-area power system is introduced, and physical constraints of the governors and turbines are considered. The model was employed in the MPC structures. Digital simulations for both two and three-area power systems are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the overall closed-loop system performance demonstrated robustness in the face of uncertainties due to governors and turbines parameters variation and loads disturbances. A performance comparison between the proposed controller and a classical integral control scheme is carried out confirming the superiority of the proposed MPC technique.

Popularity Modeling for Mobile Apps: A Sequential Approach.

Science.gov (United States)

Zhu, Hengshu; Liu, Chuanren; Ge, Yong; Xiong, Hui; Chen, Enhong

2015-07-01

The popularity information in App stores, such as chart rankings, user ratings, and user reviews, provides an unprecedented opportunity to understand user experiences with mobile Apps, learn the process of adoption of mobile Apps, and thus enables better mobile App services. While the importance of popularity information is well recognized in the literature, the use of the popularity information for mobile App services is still fragmented and under-explored. To this end, in this paper, we propose a sequential approach based on hidden Markov model (HMM) for modeling the popularity information of mobile Apps toward mobile App services. Specifically, we first propose a popularity based HMM (PHMM) to model the sequences of the heterogeneous popularity observations of mobile Apps. Then, we introduce a bipartite based method to precluster the popularity observations. This can help to learn the parameters and initial values of the PHMM efficiently. Furthermore, we demonstrate that the PHMM is a general model and can be applicable for various mobile App services, such as trend based App recommendation, rating and review spam detection, and ranking fraud detection. Finally, we validate our approach on two real-world data sets collected from the Apple Appstore. Experimental results clearly validate both the effectiveness and efficiency of the proposed popularity modeling approach.

Prediction of equibiaxial loading stress in collagen-based extracellular matrix using a three-dimensional unit cell model.

Science.gov (United States)

Susilo, Monica E; Bell, Brett J; Roeder, Blayne A; Voytik-Harbin, Sherry L; Kokini, Klod; Nauman, Eric A

2013-03-01

Mechanical signals are important factors in determining cell fate. Therefore, insights as to how mechanical signals are transferred between the cell and its surrounding three-dimensional collagen fibril network will provide a basis for designing the optimum extracellular matrix (ECM) microenvironment for tissue regeneration. Previously we described a cellular solid model to predict fibril microstructure-mechanical relationships of reconstituted collagen matrices due to unidirectional loads (Acta Biomater 2010;6:1471-86). The model consisted of representative volume elements made up of an interconnected network of flexible struts. The present study extends this work by adapting the model to account for microstructural anisotropy of the collagen fibrils and a biaxial loading environment. The model was calibrated based on uniaxial tensile data and used to predict the equibiaxial tensile stress-stretch relationship. Modifications to the model significantly improved its predictive capacity for equibiaxial loading data. With a comparable fibril length (model 5.9-8μm, measured 7.5μm) and appropriate fibril anisotropy the anisotropic model provides a better representation of the collagen fibril microstructure. Such models are important tools for tissue engineering because they facilitate prediction of microstructure-mechanical relationships for collagen matrices over a wide range of microstructures and provide a framework for predicting cell-ECM interactions. Copyright © 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Learning and Control Model of the Arm for Loading

Science.gov (United States)

Kim, Kyoungsik; Kambara, Hiroyuki; Shin, Duk; Koike, Yasuharu

We propose a learning and control model of the arm for a loading task in which an object is loaded onto one hand with the other hand, in the sagittal plane. Postural control during object interactions provides important points to motor control theories in terms of how humans handle dynamics changes and use the information of prediction and sensory feedback. For the learning and control model, we coupled a feedback-error-learning scheme with an Actor-Critic method used as a feedback controller. To overcome sensory delays, a feedforward dynamics model (FDM) was used in the sensory feedback path. We tested the proposed model in simulation using a two-joint arm with six muscles, each with time delays in muscle force generation. By applying the proposed model to the loading task, we showed that motor commands started increasing, before an object was loaded on, to stabilize arm posture. We also found that the FDM contributes to the stabilization by predicting how the hand changes based on contexts of the object and efferent signals. For comparison with other computational models, we present the simulation results of a minimum-variance model.

Finite-time adaptive sliding mode force control for electro-hydraulic load simulator based on improved GMS friction model

Science.gov (United States)

Kang, Shuo; Yan, Hao; Dong, Lijing; Li, Changchun

2018-03-01

This paper addresses the force tracking problem of electro-hydraulic load simulator under the influence of nonlinear friction and uncertain disturbance. A nonlinear system model combined with the improved generalized Maxwell-slip (GMS) friction model is firstly derived to describe the characteristics of load simulator system more accurately. Then, by using particle swarm optimization (PSO) algorithm ​combined with the system hysteresis characteristic analysis, the GMS friction parameters are identified. To compensate for nonlinear friction and uncertain disturbance, a finite-time adaptive sliding mode control method is proposed based on the accurate system model. This controller has the ability to ensure that the system state moves along the nonlinear sliding surface to steady state in a short time as well as good dynamic properties under the influence of parametric uncertainties and disturbance, which further improves the force loading accuracy and rapidity. At the end of this work, simulation and experimental results are employed to demonstrate the effectiveness of the proposed sliding mode control strategy.

A study on fatigue crack growth model considering high mean loading effects based on structural stress

International Nuclear Information System (INIS)

Kim, Jong Sung; Kim, Cheol; Jin, Tae Eun; Dong, P.

2004-01-01

The mesh-insensitive structural stress procedure by Dong is modified to apply to the welded joints with local thickness variation and inarguable shear/normal stresses along local discontinuity surface. In order to make use of the structural stress based K solution for fatigue correlation of welded joints, a proper crack growth model needs to be developed. There exist some significant discrepancies in inferring the slope or crack growth exponent in the conventional Paris law regime. Two-stage crack growth model was not considered since its applications are focused upon the fatigue behavior in welded joints in which the load ratio effects are considered negligible. In this paper, a two-stage crack growth law considering high mean loading is proposed and proven to be effective in unifying the so-called anomalous short crack growth data

Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

International Nuclear Information System (INIS)

Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian

2015-01-01

Highlights: • A novel Short Term Medium Voltage (MV) Load Forecasting (STLF) model is presented. • A knowledge-based STLF error control mechanism is implemented. • An Artificial Neural Network (ANN)-based optimum tuning is applied on STLF. • The relationship between load profiles and operational conditions is analysed. - Abstract: This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

Science.gov (United States)

Rustamov, Samir; Mustafayev, Elshan; Clements, Mark A.

2018-04-01

The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM) can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.

Context Analysis of Customer Requests using a Hybrid Adaptive Neuro Fuzzy Inference System and Hidden Markov Models in the Natural Language Call Routing Problem

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Rustamov Samir

2018-04-01

Full Text Available The context analysis of customer requests in a natural language call routing problem is investigated in the paper. One of the most significant problems in natural language call routing is a comprehension of client request. With the aim of finding a solution to this issue, the Hybrid HMM and ANFIS models become a subject to an examination. Combining different types of models (ANFIS and HMM can prevent misunderstanding by the system for identification of user intention in dialogue system. Based on these models, the hybrid system may be employed in various language and call routing domains due to nonusage of lexical or syntactic analysis in classification process.

Hidden Markov models in automatic speech recognition

Science.gov (United States)

Wrzoskowicz, Adam

1993-11-01

This article describes a method for constructing an automatic speech recognition system based on hidden Markov models (HMMs). The author discusses the basic concepts of HMM theory and the application of these models to the analysis and recognition of speech signals. The author provides algorithms which make it possible to train the ASR system and recognize signals on the basis of distinct stochastic models of selected speech sound classes. The author describes the specific components of the system and the procedures used to model and recognize speech. The author discusses problems associated with the choice of optimal signal detection and parameterization characteristics and their effect on the performance of the system. The author presents different options for the choice of speech signal segments and their consequences for the ASR process. The author gives special attention to the use of lexical, syntactic, and semantic information for the purpose of improving the quality and efficiency of the system. The author also describes an ASR system developed by the Speech Acoustics Laboratory of the IBPT PAS. The author discusses the results of experiments on the effect of noise on the performance of the ASR system and describes methods of constructing HMM's designed to operate in a noisy environment. The author also describes a language for human-robot communications which was defined as a complex multilevel network from an HMM model of speech sounds geared towards Polish inflections. The author also added mandatory lexical and syntactic rules to the system for its communications vocabulary.

H∞ Based Control for Load Mitigation in Wind Turbines

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Aron Pujana-Arrese

2012-04-01

Full Text Available This article demonstrates a strategy to design multivariable and multi-objective controllers based on the H∞ norm reduction applied to a wind turbine. The wind turbine model has been developed in the GH Bladed software and it is based on a 5 MW wind turbine defined in the Upwind European project. The designed control strategy works in the above rated power production zone and performs generator speed control and load reduction on the drive train and tower. In order to do this, two robust H∞ MISO (Multi-Input Single-Output controllers have been developed. These controllers generate collective pitch angle and generator torque set-point values to achieve the imposed control objectives. Linear models obtained in GH Bladed 4.0 are used, but the control design methodology can be used with linear models obtained from any other modelling package. Controllers are designed by setting out a mixed sensitivity problem, where some notch filters are also included in the controller dynamics. The obtained H∞ controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases. The analysis compares the proposed control strategy based on H∞ controllers to a baseline control strategy designed using the classical control methods implemented on the present wind turbines.

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

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Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

Factor Analysis of the Aggregated Electric Vehicle Load Based on Data Mining

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Yao Wang

2012-06-01

Full Text Available Electric vehicles (EVs and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably.

A hidden Markov model approach for determining expression from genomic tiling micro arrays

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Krogh Anders

2006-05-01

Full Text Available Abstract Background Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. Results We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005 tiling array experiments on ten Human chromosomes 1. Results can be downloaded and viewed from our web site 2. Conclusion The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

A Web-Based Tool to Interpolate Nitrogen Loading Using a Genetic Algorithm

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Youn Shik Park

2014-09-01

Full Text Available Water quality data may not be collected at a high frequency, nor over the range of streamflow data. For instance, water quality data are often collected monthly, biweekly, or weekly, since collecting and analyzing water quality samples are costly compared to streamflow data. Regression models are often used to interpolate pollutant loads from measurements made intermittently. Web-based Load Interpolation Tool (LOADIN was developed to provide user-friendly interfaces and to allow use of streamflow and water quality data from U.S. Geological Survey (USGS via web access. LOADIN has a regression model assuming that instantaneous load is comprised of the pollutant load based on streamflow and the pollutant load variation within the period. The regression model has eight coefficients determined by a genetic algorithm with measured water quality data. LOADIN was applied to eleven water quality datasets from USGS gage stations located in Illinois, Indiana, Michigan, Minnesota, and Wisconsin states with drainage areas from 44 km2 to 1,847,170 km2. Measured loads were calculated by multiplying nitrogen data by streamflow data associated with measured nitrogen data. The estimated nitrogen loads and measured loads were evaluated using Nash-Sutcliffe Efficiency (NSE and coefficient of determination (R2. NSE ranged from 0.45 to 0.91, and R2 ranged from 0.51 to 0.91 for nitrogen load estimation.

A Web-Based Tool to Estimate Pollutant Loading Using LOADEST

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Youn Shik Park

2015-09-01

Full Text Available Collecting and analyzing water quality samples is costly and typically requires significant effort compared to streamflow data, thus water quality data are typically collected at a low frequency. Regression models, identifying a relationship between streamflow and water quality data, are often used to estimate pollutant loads. A web-based tool using LOAD ESTimator (LOADEST as a core engine with four modules was developed to provide user-friendly interfaces and input data collection via web access. The first module requests and receives streamflow and water quality data from the U.S. Geological Survey. The second module retrieves watershed area for computation of pollutant loads per unit area. The third module examines potential error of input datasets for LOADEST runs, and the last module computes estimated and allowable annual average pollutant loads and provides tabular and graphical LOADEST outputs. The web-based tool was applied to two watersheds in this study, one agriculturally-dominated and one urban-dominated. It was found that annual sediment load at the urban-dominant watershed exceeded the target load; therefore, the web-based tool identified correctly the watershed requiring best management practices to reduce pollutant loads.

Mutual Information-Based Inputs Selection for Electric Load Time Series Forecasting

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Nenad Floranović

2013-02-01

Full Text Available Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI has been recently proposed in regression tasks, mostly for feature selection and for identifying the real instances from training sets that contains noise and outliers. This paper proposes a methodology for the training set selection in a least squares support vector machines (LS-SVMs load forecasting model. A new application of the concept of MI is presented for the selection of a training set based on MI computation between initial training set instances and testing set instances. Accordingly, several LS-SVMs models have been trained, based on the proposed methodology, for hourly prediction of electric load for one day ahead. The results obtained from a real-world data set indicate that the proposed method increases the accuracy of load forecasting as well as reduces the size of the initial training set needed for model training.

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

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Hong-Juan Li

2013-04-01

Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

Clinical Prediction Performance of Glaucoma Progression Using a 2-Dimensional Continuous-Time Hidden Markov Model with Structural and Functional Measurements.

Science.gov (United States)

Song, Youngseok; Ishikawa, Hiroshi; Wu, Mengfei; Liu, Yu-Ying; Lucy, Katie A; Lavinsky, Fabio; Liu, Mengling; Wollstein, Gadi; Schuman, Joel S

2018-03-20

Previously, we introduced a state-based 2-dimensional continuous-time hidden Markov model (2D CT HMM) to model the pattern of detected glaucoma changes using structural and functional information simultaneously. The purpose of this study was to evaluate the detected glaucoma change prediction performance of the model in a real clinical setting using a retrospective longitudinal dataset. Longitudinal, retrospective study. One hundred thirty-four eyes from 134 participants diagnosed with glaucoma or as glaucoma suspects (average follow-up, 4.4±1.2 years; average number of visits, 7.1±1.8). A 2D CT HMM model was trained using OCT (Cirrus HD-OCT; Zeiss, Dublin, CA) average circumpapillary retinal nerve fiber layer (cRNFL) thickness and visual field index (VFI) or mean deviation (MD; Humphrey Field Analyzer; Zeiss). The model was trained using a subset of the data (107 of 134 eyes [80%]) including all visits except for the last visit, which was used to test the prediction performance (training set). Additionally, the remaining 27 eyes were used for secondary performance testing as an independent group (validation set). The 2D CT HMM predicts 1 of 4 possible detected state changes based on 1 input state. Prediction accuracy was assessed as the percentage of correct prediction against the patient's actual recorded state. In addition, deviations of the predicted long-term detected change paths from the actual detected change paths were measured. Baseline mean ± standard deviation age was 61.9±11.4 years, VFI was 90.7±17.4, MD was -3.50±6.04 dB, and cRNFL thickness was 74.9±12.2 μm. The accuracy of detected glaucoma change prediction using the training set was comparable with the validation set (57.0% and 68.0%, respectively). Prediction deviation from the actual detected change path showed stability throughout patient follow-up. The 2D CT HMM demonstrated promising prediction performance in detecting glaucoma change performance in a simulated clinical setting

Learning with Admixture: Modeling, Optimization, and Applications in Population Genetics

DEFF Research Database (Denmark)

Cheng, Jade Yu

2016-01-01

the foundation for both CoalHMM and Ohana. Optimization modeling has been the main theme throughout my PhD, and it will continue to shape my work for the years to come. The algorithms and software I developed to study historical admixture and population evolution fall into a larger family of machine learning...... geneticists strive to establish working solutions to extract information from massive volumes of biological data. The steep increase in the quantity and quality of genomic data during the past decades provides a unique opportunity but also calls for new and improved algorithms and software to cope...... including population splits, effective population sizes, gene flow, etc. Since joining the CoalHMM development team in 2014, I have mainly contributed in two directions: 1) improving optimizations through heuristic-based evolutionary algorithms and 2) modeling of historical admixture events. Ohana, meaning...

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

International Nuclear Information System (INIS)

Aalami, H.A.; Moghaddam, M. Parsa; Yousefi, G.R.

2010-01-01

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority. (author)

Modelling of Generic Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; Bendtsen, Jan Dimon; La Cour-Harbo, Anders

2006-01-01

of Least Constraint using the Udwadia-Kalaba equation and can be used to model all body to body slung load suspension types. The model gives an intuitive and easy-to-use way of modelling and simulating di erent slung load suspension types and it includes detection and response of wire slacking...

Dynamic intelligent cleaning model of dirty electric load data

International Nuclear Information System (INIS)

Zhang Xiaoxing; Sun Caixin

2008-01-01

There are a number of dirty data in the load database derived from the supervisory control and data acquisition (SCADA) system. Thus, the data must be carefully and reasonably adjusted before it is used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent data cleaning model based on data mining theory. Firstly, on the basis of fuzzy soft clustering, the Kohonen clustering network is improved to fulfill the parallel calculation of fuzzy c-means soft clustering. Then, the proposed dynamic algorithm can automatically find the new clustering center (the characteristic curve of the data) with the updated sample data; At last, it is composed with radial basis function neural network (RBFNN), and then, an intelligent adjusting model is proposed to identify the dirty data. The rapid and dynamic performance of the model makes it suitable for real time calculation, and the efficiency and accuracy of the model is proved by test results of electrical load data analysis in Chongqing

Evolving the structure of hidden Markov Models

DEFF Research Database (Denmark)

won, K. J.; Prugel-Bennett, A.; Krogh, A.

2006-01-01

A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission...... and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature....

Detecting Structural Breaks using Hidden Markov Models

DEFF Research Database (Denmark)

Ntantamis, Christos

Testing for structural breaks and identifying their location is essential for econometric modeling. In this paper, a Hidden Markov Model (HMM) approach is used in order to perform these tasks. Breaks are defined as the data points where the underlying Markov Chain switches from one state to another....... The estimation of the HMM is conducted using a variant of the Iterative Conditional Expectation-Generalized Mixture (ICE-GEMI) algorithm proposed by Delignon et al. (1997), that permits analysis of the conditional distributions of economic data and allows for different functional forms across regimes...

A model for Quick Load Analysis for monopile-type offshore wind turbine substructures

DEFF Research Database (Denmark)

Schløer, Signe; Castillo, Laura Garcia; Fejerskov, Morten

2016-01-01

A model for Quick Load Analysis, QuLA, of an offshore wind turbine substructure is presented. The aerodynamic rotor loads and damping are precomputed for a load-based configuration. The dynamic structural response is represented by the first global fore-aft mode only and is computed...... in the frequency domain using the equation of motion. The model is compared against the state of the art aeroelastic code, Flex5, and both life time fatigue and extreme loads are considered in the comparison. In general there is good similarity between the two models. Some derivation for the sectional forces...... are explained in terms of the model simplifications. The difference in the sectional moments are found to be within 14% for the fatigue load case and 10% for the extreme load condition....

INVESTIGATION OF STRAIN STATE OF BASE OF THE FRAGMENT’S MODEL OF THE PILE FOUNDATION DURING STAGED LOADING OF ITS ELEMENTS IN THE SOIL TANK

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SEDIN V. L.

2017-04-01

Full Text Available Summary. Purpose. Changes of stress strain state of the fragment’s model of the pile foundation’s base depending on load sequence change should be investigated. Methodology. There are laboratory three test runs of the fragment’s model of the pile foundation carried out at the soil tank, unlike staged of loading and square of weight square on the soil under the plate raft with the including of pile. Results. There are laboratory confirmations of change of strain-stress state of pile foundation base depending on its elements’ load sequence change and confirmations of the influence of piles preloading on their settlement reduction. Originality. Carried out series of tests under the spatial invert with step-by-step loading of model elements of pile foundation fragments (the II and III test runs showed that such sequence of foundation load allows it to take heavier loads under the condition of the same values of settlement. The model of pile foundation’s fragment with additional loading of pile (III test run showed that such sequence of foundation load allows it to take heavier loads in comparison to the I test run. Practical value. Upon condition of staged including of all elements of the pile foundation, prestressed strain state (additional skin frictions, around the model of the pile, at some point, which facilitates the movement of the pile’s model, not connected with the plate. There are laboratory confirmations of dependencies that the more the weight square under the plate is, the greater the displacement of pile’s fragment. This effect can be useful for preloading piles in the real building, and to help align the piles' settling within the limits of the pile field, before the transfer of operational loads.

A dynamic modelling approach for estimating critical loads of nitrogen based on plant community changes under a changing climate

International Nuclear Information System (INIS)

Belyazid, Salim; Kurz, Dani; Braun, Sabine; Sverdrup, Harald; Rihm, Beat; Hettelingh, Jean-Paul

2011-01-01

A dynamic model of forest ecosystems was used to investigate the effects of climate change, atmospheric deposition and harvest intensity on 48 forest sites in Sweden (n = 16) and Switzerland (n = 32). The model was used to investigate the feasibility of deriving critical loads for nitrogen (N) deposition based on changes in plant community composition. The simulations show that climate and atmospheric deposition have comparably important effects on N mobilization in the soil, as climate triggers the release of organically bound nitrogen stored in the soil during the elevated deposition period. Climate has the most important effect on plant community composition, underlining the fact that this cannot be ignored in future simulations of vegetation dynamics. Harvest intensity has comparatively little effect on the plant community in the long term, while it may be detrimental in the short term following cutting. This study shows: that critical loads of N deposition can be estimated using the plant community as an indicator; that future climatic changes must be taken into account; and that the definition of the reference deposition is critical for the outcome of this estimate. - Research highlights: → Plant community changes can be used to estimate critical loads of nitrogen. → Climate change is decisive for future changes of geochemistry and plant communities. → Climate change cannot be ignored in estimates of critical loads. → The model ForSAFE-Veg was successfully used to set critical loads of nitrogen. - Plant community composition can be used in dynamic modelling to estimate critical loads of nitrogen deposition, provided the appropriate reference deposition, future climate and target plant communities are defined.

Under-Frequency Load Shedding Technique Considering Event-Based for an Islanded Distribution Network

Directory of Open Access Journals (Sweden)

Hasmaini Mohamad

2016-06-01

Full Text Available One of the biggest challenge for an islanding operation is to sustain the frequency stability. A large power imbalance following islanding would cause under-frequency, hence an appropriate control is required to shed certain amount of load. The main objective of this research is to develop an adaptive under-frequency load shedding (UFLS technique for an islanding system. The technique is designed considering an event-based which includes the moment system is islanded and a tripping of any DG unit during islanding operation. A disturbance magnitude is calculated to determine the amount of load to be shed. The technique is modeled by using PSCAD simulation tool. A simulation studies on a distribution network with mini hydro generation is carried out to evaluate the UFLS model. It is performed under different load condition: peak and base load. Results show that the load shedding technique have successfully shed certain amount of load and stabilized the system frequency.

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Energy Technology Data Exchange (ETDEWEB)

Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)

2008-09-15

This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)

Intonation model for TTS in Sepedi

CSIR Research Space (South Africa)

Van Niekerk, DR

2010-09-01

Full Text Available the size of the tone-marked corpus does not lend itself to a comprehensive statistical analysis of the comparison results, we have identified a number of characteristics consis- tently exhibited in the natural F0 contours not accounted for in the tone... in the tone-marked set was synthesised with excita- tion signals derived from the standard HMM-based models, the tone-based model and the linearly declining contours discussed above. Listeners were asked to rate each sample using integers ranging from 1...

Short term load forecasting: two stage modelling

Directory of Open Access Journals (Sweden)

SOARES, L. J.

2009-06-01

Full Text Available This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive model proposed by Soares (2003 using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.

Understanding eye movements in face recognition using hidden Markov models.

Science.gov (United States)

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2014-09-16

We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone. © 2014 ARVO.

Final Project Report Load Modeling Transmission Research

Energy Technology Data Exchange (ETDEWEB)

Lesieutre, Bernard [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bravo, Richard [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Yinger, Robert [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chassin, Dave [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Huang, Henry [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lu, Ning [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Hiskens, Ian [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Venkataramanan, Giri [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2012-03-31

The research presented in this report primarily focuses on improving power system load models to better represent their impact on system behavior. The previous standard load model fails to capture the delayed voltage recovery events that are observed in the Southwest and elsewhere. These events are attributed to stalled air conditioner units after a fault. To gain a better understanding of their role in these events and to guide modeling efforts, typical air conditioner units were testing in laboratories. Using data obtained from these extensive tests, new load models were developed to match air conditioner behavior. An air conditioner model is incorporated in the new WECC composite load model. These models are used in dynamic studies of the West and can impact power transfer limits for California. Unit-level and systemlevel solutions are proposed as potential solutions to the delayed voltage recovery problem.

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

Science.gov (United States)

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

Comparison of Building Energy Modeling Programs: Building Loads

Energy Technology Data Exchange (ETDEWEB)

Zhu, Dandan [Tsinghua Univ., Beijing (China); Hong, Tianzhen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Yan, Da [Tsinghua Univ., Beijing (China); Wang, Chuang [Tsinghua Univ., Beijing (China)

2012-06-01

This technical report presented the methodologies, processes, and results of comparing three Building Energy Modeling Programs (BEMPs) for load calculations: EnergyPlus, DeST and DOE-2.1E. This joint effort, between Lawrence Berkeley National Laboratory, USA and Tsinghua University, China, was part of research projects under the US-China Clean Energy Research Center on Building Energy Efficiency (CERC-BEE). Energy Foundation, an industrial partner of CERC-BEE, was the co-sponsor of this study work. It is widely known that large discrepancies in simulation results can exist between different BEMPs. The result is a lack of confidence in building simulation amongst many users and stakeholders. In the fields of building energy code development and energy labeling programs where building simulation plays a key role, there are also confusing and misleading claims that some BEMPs are better than others. In order to address these problems, it is essential to identify and understand differences between widely-used BEMPs, and the impact of these differences on load simulation results, by detailed comparisons of these BEMPs from source code to results. The primary goal of this work was to research methods and processes that would allow a thorough scientific comparison of the BEMPs. The secondary goal was to provide a list of strengths and weaknesses for each BEMP, based on in-depth understandings of their modeling capabilities, mathematical algorithms, advantages and limitations. This is to guide the use of BEMPs in the design and retrofit of buildings, especially to support China’s building energy standard development and energy labeling program. The research findings could also serve as a good reference to improve the modeling capabilities and applications of the three BEMPs. The methodologies, processes, and analyses employed in the comparison work could also be used to compare other programs. The load calculation method of each program was analyzed and compared to

[Analysis on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed based on L-THIA model].

Science.gov (United States)

Li, Kai; Zeng, Fan-Tang; Fang, Huai-Yang; Lin, Shu

2013-11-01

Based on the Long-term Hydrological Impact Assessment (L-THIA) model, the effect of land use and rainfall change on nitrogen and phosphorus loading of non-point sources in Shiqiao river watershed was analyzed. The parameters in L-THIA model were revised according to the data recorded in the scene of runoff plots, which were set up in the watershed. The results showed that the distribution of areas with high pollution load was mainly concentrated in agricultural land and urban land. Agricultural land was the biggest contributor to nitrogen and phosphorus load. From 1995 to 2010, the load of major pollutants, namely TN and TP, showed an obviously increasing trend with increase rates of 17.91% and 25.30%, respectively. With the urbanization in the watershed, urban land increased rapidly and its area proportion reached 43.94%. The contribution of urban land to nitrogen and phosphorus load was over 40% in 2010. This was the main reason why pollution load still increased obviously while the agricultural land decreased greatly in the past 15 years. The rainfall occurred in the watershed was mainly concentrated in the flood season, so the nitrogen and phosphorus load of the flood season was far higher than that of the non-flood season and the proportion accounting for the whole year was over 85%. Pearson regression analysis between pollution load and the frequency of different patterns of rainfall demonstrated that rainfall exceeding 20 mm in a day was the main rainfall type causing non-point source pollution.

Inference with constrained hidden Markov models in PRISM

DEFF Research Database (Denmark)

Christiansen, Henning; Have, Christian Theil; Lassen, Ole Torp

2010-01-01

A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference. De......_different are integrated. We experimentally validate our approach on the biologically motivated problem of global pairwise alignment.......A Hidden Markov Model (HMM) is a common statistical model which is widely used for analysis of biological sequence data and other sequential phenomena. In the present paper we show how HMMs can be extended with side-constraints and present constraint solving techniques for efficient inference...

Model of wind shear conditional on turbulence and its impact on wind turbine loads

DEFF Research Database (Denmark)

Dimitrov, Nikolay Krasimirov; Natarajan, Anand; Kelly, Mark C.

2015-01-01

proposed for flat terrain and that can significantly decrease the uncertainty associated with fatigue load predictions for wind turbines with large rotors. An essential contribution is the conditioning of wind shear on the 90% quantile of wind turbulence, such that the appropriate magnitude of the design...... fatigue load is achieved. The proposed wind shear model based on the wind measurements is thereby probabilistic in definition, with shear jointly distributed with wind turbulence. A simplified model for the wind shear exponent is further derived from the full stochastic model. The fatigue loads over...... is most pronounced on the blade flap loads. It is further shown that under moderate wind turbulence, the wind shear exponents may be over-specified in the design standards, and a reduction of wind shear exponent based on the present measurements can contribute to reduced fatigue damage equivalent loads...

Fiber Bundle Model Under Heterogeneous Loading

Science.gov (United States)

Roy, Subhadeep; Goswami, Sanchari

2018-03-01

The present work deals with the behavior of fiber bundle model under heterogeneous loading condition. The model is explored both in the mean-field limit as well as with local stress concentration. In the mean field limit, the failure abruptness decreases with increasing order k of heterogeneous loading. In this limit, a brittle to quasi-brittle transition is observed at a particular strength of disorder which changes with k. On the other hand, the model is hardly affected by such heterogeneity in the limit where local stress concentration plays a crucial role. The continuous limit of the heterogeneous loading is also studied and discussed in this paper. Some of the important results related to fiber bundle model are reviewed and their responses to our new scheme of heterogeneous loading are studied in details. Our findings are universal with respect to the nature of the threshold distribution adopted to assign strength to an individual fiber.

A systematic hub loads model of a horizontal wind turbine

International Nuclear Information System (INIS)

Kazacoks, Romans; Jamieson, Peter

2014-01-01

The wind turbine industry has focused offshore on increasing the capacity of a single unit through up-scaling their machines. There is however a lack of systematic studies on how loads vary due to properties of a wind turbine and scaling of wind turbines. The purpose of this paper is to study how applied blade modifications, with similarities such as mass, stiffness and dimensions, influence blade root moments and lifetime damage equivalent loads (DELs) of the rotor blades. In order to produce fatigue load blade root moment trends based on the applied modifications. It was found that a linear trend of lifetime DELs based on the applied modifications of blades, which have effect on the natural frequency of blade of the original or reference model. As the control system was tuned for the specific frequency of the reference model. The linear trend of lifetime DELs was generated as long as the natural frequency of the reference model was preserved. For larger modifications of the wind turbine the controller would need retuning

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2015-01-01

Full Text Available In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.

Probabilistic model of bridge vehicle loads in port area based on in-situ load testing

Science.gov (United States)

Deng, Ming; Wang, Lei; Zhang, Jianren; Wang, Rei; Yan, Yanhong

2017-11-01

Vehicle load is an important factor affecting the safety and usability of bridges. An statistical analysis is carried out in this paper to investigate the vehicle load data of Tianjin Haibin highway in Tianjin port of China, which are collected by the Weigh-in- Motion (WIM) system. Following this, the effect of the vehicle load on test bridge is calculated, and then compared with the calculation result according to HL-93(AASHTO LRFD). Results show that the overall vehicle load follows a distribution with a weighted sum of four normal distributions. The maximum vehicle load during the design reference period follows a type I extremum distribution. The vehicle load effect also follows a weighted sum of four normal distributions, and the standard value of the vehicle load is recommended as 1.8 times that of the calculated value according to HL-93.

Passive acoustic leak detection for sodium cooled fast reactors using hidden Markov models

Energy Technology Data Exchange (ETDEWEB)

Riber Marklund, A. [CEA, Cadarache, DEN/DTN/STCP/LIET, Batiment 202, 13108 St Paul-lez-Durance, (France); Kishore, S. [Fast Reactor Technology Group of IGCAR, (India); Prakash, V. [Vibrations Diagnostics Division, Fast Reactor Technology Group of IGCAR, (India); Rajan, K.K. [Fast Reactor Technology Group and Engineering Services Group of IGCAR, (India)

2015-07-01

Acoustic leak detection for steam generators of sodium fast reactors have been an active research topic since the early 1970's and several methods have been tested over the years. Inspired by its success in the field of automatic speech recognition, we here apply hidden Markov models (HMM) in combination with Gaussian mixture models (GMM) to the problem. To achieve this, we propose a new feature calculation scheme, based on the temporal evolution of the power spectral density (PSD) of the signal. Using acoustic signals recorded during steam/water injection experiments done at the Indira Gandhi Centre for Atomic Research (IGCAR), the proposed method is tested. We perform parametric studies on the HMM+GMM model size and demonstrate that the proposed method a) performs well without a priori knowledge of injection noise, b) can incorporate several noise models and c) has an output distribution that simplifies false alarm rate control. (authors)

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

On a model for the prediction of the friction coefficient in mixed lubrication based on a load-sharing concapt with measured surface roughness

NARCIS (Netherlands)

Akchurin, Aydar; Bosman, Rob; Lugt, Pieter Martin; van Drogen, Mark

2015-01-01

A new model was developed for the simulation of the friction coefficient in lubricated sliding line contacts. A half-space-based contact algorithm was linked with a numerical elasto-hydrodynamic lubrication solver using the load-sharing concept. The model was compared with an existing asperity-based

Load-Sharing Characteristics of Power-Split Transmission System Based on Deformation Compatibility and Loaded Tooth Contact Analysis

Directory of Open Access Journals (Sweden)

Hao Dong

2015-01-01

Full Text Available In order to implement the uniform load distribution of the power-split transmission system, a pseudostatic model is built. Based on the loaded tooth contact analysis (LTCA technique, the actual meshing process of each gear pair is simulated and the fitting curve of time-varying mesh stiffness is obtained. And then, the torsional angle deformation compatibility conditions are proposed according to the closed-loop characteristic of power flow, which will be combined with the torque equilibrium conditions and elastic support conditions to calculate the transfer torque of each gear pair. Finally, the load-sharing coefficient of the power-split transmission system is obtained, and the influences of the installation errors are analyzed. The results show that the above-mentioned installation errors comprehensively influence the load-sharing characteristics, and the reduction of only one error could not effectively achieve perfect load-sharing characteristics. Allowing for the spline clearance floating and constrained by the radial spacing ring, the influence of the floating pinion is analyzed. It shows that the floating pinion can improve the load-sharing characteristics. Through the comparison between the theoretical and related experimental data, the reasonability and feasibility of the above-proposed method and model are verified.

A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading

Directory of Open Access Journals (Sweden)

Foo Say Wei

2005-01-01

Full Text Available Hidden Markov model (HMM has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.

Modelling and Simulation of Wave Loads

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

velocity can be approximated by a Gaussian Markov process. Known approximate results for the first-passage density or equivalently, the distribution of the extremes of wave loads are presented and compared with rather precise simulation results. It is demonstrated that the approximate results......A simple model of the wave load on slender members of offshore structures is described. The wave elevation of the sea state is modelled by a stationary Gaussian process. A new procedure to simulate realizations of the wave loads is developed. The simulation method assumes that the wave particle...

Modelling and Simulation of Wave Loads

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard; Thoft-Christensen, Palle

1985-01-01

velocity can be approximated by a Gaussian Markov process. Known approximate results for the first passage density or equivalently, the distribution of the extremes of wave loads are presented and compared with rather precise simulation results. It is demonstrated that the approximate results......A simple model of the wave load on stender members of offshore structures is described . The wave elevation of the sea stateis modelled by a stationary Gaussian process. A new procedure to simulate realizations of the wave loads is developed. The simulation method assumes that the wave particle...

Modeling heterogeneous populations of thermostatically controlled loads using diffusion-advection PDEs

DEFF Research Database (Denmark)

Moura, Scott; Ruiz, Victor; Bendtsen, Jan Dimon

2013-01-01

This paper focuses on developing a partial differential equation (PDE)-based model and parameter identification scheme for heterogeneous populations of thermostatically controlled loads (TCLs). First, a coupled two-state hyperbolic PDE model for homogenous TCL populations is derived. This model i...

A servo controlled gradient loading triaxial model test system for deep-buried cavern

Energy Technology Data Exchange (ETDEWEB)

Chen, Xu-guang [College of Engineering, Ocean University of China, Qingdao 266100 (China); Shandong Provincial Key Laboratory of Ocean Engineering, Qingdao 266100 (China); Research Center of Geotechnical and Structural Engineering, Shandong University, Jinan 250061 (China); Zhang, Qiang-yong; Li, Shu-cai [Research Center of Geotechnical and Structural Engineering, Shandong University, Jinan 250061 (China)

2015-10-15

A servo controlled gradient loading model test system is developed to simulate the gradient geostress in deep-buried cavern. This system consists of the gradient loading apparatus, the digital servo control device, and the measurement system. Among them, the gradient loading apparatus is the main component which is used for exerting load onto the model. This loading apparatus is placed inside the counterforce wall/beam and is divided to several different loading zones, with each loading zone independently controlled. This design enables the gradient loading. Hence, the “real” geostress field surrounding the deep-buried cavern can be simulated. The loading or unloading process can be controlled by the human-computer interaction machines, i.e., the digital servo control system. It realizes the automation and visualization of model loading/unloading. In addition, this digital servo could control and regulate hydraulic loading instantaneously, which stabilizes the geostress onto the model over a long term. During the loading procedure, the collision between two adjacent loading platens is also eliminated by developing a guide frame. This collision phenomenon is induced by the volume shrinkage of the model when compressed in true 3D state. In addition, several accurate measurements, including the optical and grating-based method, are adopted to monitor the small deformation of the model. Hence, the distortion of the model could be accurately measured. In order to validate the performance of this innovative model test system, a 3D geomechanical test was conducted on a simulated deep-buried underground reservoir. The result shows that the radial convergence increases rapidly with the release of the stress in the reservoir. Moreover, the deformation increases with the increase of the gas production rate. This observation is consistence with field observation in petroleum engineering. The system is therefore capable of testing deep-buried engineering structures.

A servo controlled gradient loading triaxial model test system for deep-buried cavern.

Science.gov (United States)

Chen, Xu-guang; Zhang, Qiang-yong; Li, Shu-cai

2015-10-01

A servo controlled gradient loading model test system is developed to simulate the gradient geostress in deep-buried cavern. This system consists of the gradient loading apparatus, the digital servo control device, and the measurement system. Among them, the gradient loading apparatus is the main component which is used for exerting load onto the model. This loading apparatus is placed inside the counterforce wall/beam and is divided to several different loading zones, with each loading zone independently controlled. This design enables the gradient loading. Hence, the "real" geostress field surrounding the deep-buried cavern can be simulated. The loading or unloading process can be controlled by the human-computer interaction machines, i.e., the digital servo control system. It realizes the automation and visualization of model loading/unloading. In addition, this digital servo could control and regulate hydraulic loading instantaneously, which stabilizes the geostress onto the model over a long term. During the loading procedure, the collision between two adjacent loading platens is also eliminated by developing a guide frame. This collision phenomenon is induced by the volume shrinkage of the model when compressed in true 3D state. In addition, several accurate measurements, including the optical and grating-based method, are adopted to monitor the small deformation of the model. Hence, the distortion of the model could be accurately measured. In order to validate the performance of this innovative model test system, a 3D geomechanical test was conducted on a simulated deep-buried underground reservoir. The result shows that the radial convergence increases rapidly with the release of the stress in the reservoir. Moreover, the deformation increases with the increase of the gas production rate. This observation is consistence with field observation in petroleum engineering. The system is therefore capable of testing deep-buried engineering structures.

A servo controlled gradient loading triaxial model test system for deep-buried cavern

International Nuclear Information System (INIS)

Chen, Xu-guang; Zhang, Qiang-yong; Li, Shu-cai

2015-01-01

A servo controlled gradient loading model test system is developed to simulate the gradient geostress in deep-buried cavern. This system consists of the gradient loading apparatus, the digital servo control device, and the measurement system. Among them, the gradient loading apparatus is the main component which is used for exerting load onto the model. This loading apparatus is placed inside the counterforce wall/beam and is divided to several different loading zones, with each loading zone independently controlled. This design enables the gradient loading. Hence, the “real” geostress field surrounding the deep-buried cavern can be simulated. The loading or unloading process can be controlled by the human-computer interaction machines, i.e., the digital servo control system. It realizes the automation and visualization of model loading/unloading. In addition, this digital servo could control and regulate hydraulic loading instantaneously, which stabilizes the geostress onto the model over a long term. During the loading procedure, the collision between two adjacent loading platens is also eliminated by developing a guide frame. This collision phenomenon is induced by the volume shrinkage of the model when compressed in true 3D state. In addition, several accurate measurements, including the optical and grating-based method, are adopted to monitor the small deformation of the model. Hence, the distortion of the model could be accurately measured. In order to validate the performance of this innovative model test system, a 3D geomechanical test was conducted on a simulated deep-buried underground reservoir. The result shows that the radial convergence increases rapidly with the release of the stress in the reservoir. Moreover, the deformation increases with the increase of the gas production rate. This observation is consistence with field observation in petroleum engineering. The system is therefore capable of testing deep-buried engineering structures

A Simple Hybrid Model for Short-Term Load Forecasting

Directory of Open Access Journals (Sweden)

Suseelatha Annamareddi

2013-01-01

Full Text Available The paper proposes a simple hybrid model to forecast the electrical load data based on the wavelet transform technique and double exponential smoothing. The historical noisy load series data is decomposed into deterministic and fluctuation components using suitable wavelet coefficient thresholds and wavelet reconstruction method. The variation characteristics of the resulting series are analyzed to arrive at reasonable thresholds that yield good denoising results. The constitutive series are then forecasted using appropriate exponential adaptive smoothing models. A case study performed on California energy market data demonstrates that the proposed method can offer high forecasting precision for very short-term forecasts, considering a time horizon of two weeks.

Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.

Science.gov (United States)

Yaghouby, Farid; Modur, Pradeep; Sunderam, Sridhar

2014-01-01

Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).

Argos: Residential end-use simulation model for load management strategy analysis

International Nuclear Information System (INIS)

Capasso, A.; Lamedica, R.; Prudenzi, A.

1992-01-01

In recent years, load management (LM) strategies, aimed at the optimization of available energy resources, as well as, the reduction of investments for new power plants, have been applied worldwide in residential end-use assessments. However, the forecasting of LM strategy impacts on the residential sector is very complex because it is based on a preliminary evaluation of the customers' proclivity to adapt their load characteristics to utility aims. In order to reduce load analysis requirements, which are substantial due to the need for thorough statistical analyses, complex field tests and measurements, the availability of models taking into account customer behavioural aspects is of paramount importance. This paper illustrates a simulation model which allows the performance of numerical evaluations concerning the effectiveness of some LM strategies applied to a residential end-use area load profile as previously determined by the aggregation of the contributions of individual households. This model enabled the evaluation of the impact, on the load profile, of a time-of-day tariff such as that recently introduced in Italy

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network

International Nuclear Information System (INIS)

Yu, Feng; Xu, Xiaozhong

2014-01-01

Highlights: • A detailed data processing will make more accurate results prediction. • Taking a full account of more load factors to improve the prediction precision. • Improved BP network obtains higher learning convergence. • Genetic algorithm optimized by chaotic cat map enhances the global search ability. • The combined GA–BP model improved by modified additional momentum factor is superior to others. - Abstract: This paper proposes an appropriate combinational approach which is based on improved BP neural network for short-term gas load forecasting, and the network is optimized by the real-coded genetic algorithm. Firstly, several kinds of modifications are carried out on the standard neural network to accelerate the convergence speed of network, including improved additional momentum factor, improved self-adaptive learning rate and improved momentum and self-adaptive learning rate. Then, it is available to use the global search capability of optimized genetic algorithm to determine the initial weights and thresholds of BP neural network to avoid being trapped in local minima. The ability of GA is enhanced by cat chaotic mapping. In light of the characteristic of natural gas load for Shanghai, a series of data preprocessing methods are adopted and more comprehensive load factors are taken into account to improve the prediction accuracy. Such improvements facilitate forecasting efficiency and exert maximum performance of the model. As a result, the integration model improved by modified additional momentum factor gets more ideal solutions for short-term gas load forecasting, through analyses and comparisons of the above several different combinational algorithms

Modeling of the Jacked Pile Static Load Test with PLAX 3D

Directory of Open Access Journals (Sweden)

Tautvydas Statkus

2016-12-01

Full Text Available In this article jacked pile installation technology and its current processes, altering the base physical and mechanical characteristics are discussed. For the jacked pile static load test simulation Plax 3D software was selected, the opportunities and developments were described. Model building, materials, models, model geometry, finite elements, boundary conditions and assumptions adopted in addressing problems described in detail. Three different tasks formulated and load-settlement dependence a comparison of the results with the experiment given. Conclusions are formulated according to the modeling results.

A constitutive model for AS4/PEEK thermoplastic composites under cyclic loading

Science.gov (United States)

Rui, Yuting; Sun, C. T.

1990-01-01

Based on the basic and essential features of the elastic-plastic response of the AS4/PEEK thermoplastic composite subjected to off-axis cyclic loadings, a simple rate-independent constitutive model is proposed to describe the orthotropic material behavior for cyclic loadings. A one-parameter memory surface is introduced to distinguish the virgin deformation and the subsequent deformation process and to characterize the loading range effect. Cyclic softening is characterized by the change of generalized plastic modulus. By the vanishing yield surface assumption, a yield criterion is not needed and it is not necessary to consider loading and unloading separately. The model is compared with experimental results and good agreement is obtained.

Model-based Diagnostics for Propellant Loading Systems

Data.gov (United States)

National Aeronautics and Space Administration — The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that...

Development of Load Duration Curve System in Data Scarce Watersheds Based on a Distributed Hydrological Model

Science.gov (United States)

WANG, J.

2017-12-01

In stream water quality control, the total maximum daily load (TMDL) program is very effective. However, the load duration curves (LDC) of TMDL are difficult to be established because no sufficient observed flow and pollutant data can be provided in data-scarce watersheds in which no hydrological stations or consecutively long-term hydrological data are available. Although the point sources or a non-point sources of pollutants can be clarified easily with the aid of LDC, where does the pollutant come from and to where it will be transported in the watershed cannot be traced by LDC. To seek out the best management practices (BMPs) of pollutants in a watershed, and to overcome the limitation of LDC, we proposed to develop LDC based on a distributed hydrological model of SWAT for the water quality management in data scarce river basins. In this study, firstly, the distributed hydrological model of SWAT was established with the scarce-hydrological data. Then, the long-term daily flows were generated with the established SWAT model and rainfall data from the adjacent weather station. Flow duration curves (FDC) was then developed with the aid of generated daily flows by SWAT model. Considering the goal of water quality management, LDC curves of different pollutants can be obtained based on the FDC. With the monitored water quality data and the LDC curves, the water quality problems caused by the point or non-point source pollutants in different seasons can be ascertained. Finally, the distributed hydrological model of SWAT was employed again to tracing the spatial distribution and the origination of the pollutants of coming from what kind of agricultural practices and/or other human activities. A case study was conducted in the Jian-jiang river, a tributary of Yangtze river, of Duyun city, Guizhou province. Results indicate that this kind of method can realize the water quality management based on TMDL and find out the suitable BMPs for reducing pollutant in a watershed.

A New Wind Turbine Generating System Model for Balanced and Unbalanced Distribution Systems Load Flow Analysis

Directory of Open Access Journals (Sweden)

Ahmet Koksoy

2018-03-01

Full Text Available Wind turbine generating systems (WTGSs, which are conventionally connected to high voltage transmission networks, have frequently been employed as distributed generation units in today’s distribution networks. In practice, the distribution networks always have unbalanced bus voltages and line currents due to uneven distribution of single or double phase loads over three phases and asymmetry of the lines, etc. Accordingly, in this study, for the load flow analysis of the distribution networks, Conventional Fixed speed Induction Generator (CFIG based WTGS, one of the most widely used WTGS types, is modelled under unbalanced voltage conditions. The Developed model has active and reactive power expressions in terms of induction machine impedance parameters, terminal voltages and input power. The validity of the Developed model is confirmed with the experimental results obtained in a test system. The results of the slip calculation based phase-domain model (SCP Model, which was previously proposed in the literature for CFIG based WTGSs under unbalanced voltages, are also given for the comparison. Finally, the Developed model and the SCP model are implemented in the load flow analysis of the IEEE 34 bus test system with the CFIG based WTGSs and unbalanced loads. Thus, it is clearly pointed out that the results of the load flow analysis implemented with both models are very close to each other, and the Developed model is computationally more efficient than the SCP model.

Bayesian Modelling of fMRI Time Series

DEFF Research Database (Denmark)

Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward

2000-01-01

We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...... Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments....

Load-Flow in Multiphase Distribution Networks: Existence, Uniqueness, Non-Singularity, and Linear Models

Energy Technology Data Exchange (ETDEWEB)

Bernstein, Andrey [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhao, Changhong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Wang, Cong [Ecole Polytechnique Federale de Lausanne (EPFL); Le Boudec, Jean-Yves [Ecole Polytechnique Federale de Lausanne (EPFL)

2018-04-06

This paper considers unbalanced multiphase distribution systems with generic topology and different load models, and extends the Z-bus iterative load-flow algorithm based on a fixed-point interpretation of the AC load-flow equations. Explicit conditions for existence and uniqueness of load-flow solutions are presented. These conditions also guarantee convergence of the load-flow algorithm to the unique solution. The proposed methodology is applicable to generic systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. Further, a sufficient condition for the non-singularity of the load-flow Jacobian is proposed. Finally, linear load-flow models are derived, and their approximation accuracy is analyzed. Theoretical results are corroborated through experiments on IEEE test feeders.

Influence of Hydraulic Design on Stability and on Pressure Pulsations in Francis Turbines at Overload, Part Load and Deep Part Load based on Numerical Simulations and Experimental Model Test Results

International Nuclear Information System (INIS)

Magnoli, M V; Maiwald, M

2014-01-01

Francis turbines have been running more and more frequently in part load conditions, in order to satisfy the new market requirements for more dynamic and flexible energy generation, ancillary services and grid regulation. The turbines should be able to be operated for longer durations with flows below the optimum point, going from part load to deep part load and even speed-no-load. These operating conditions are characterised by important unsteady flow phenomena taking place at the draft tube cone and in the runner channels, in the respective cases of part load and deep part load. The current expectations are that new Francis turbines present appropriate hydraulic stability and moderate pressure pulsations at overload, part load, deep part load and speed-no-load with high efficiency levels at normal operating range. This study presents series of investigations performed by Voith Hydro with the objective to improve the hydraulic stability of Francis turbines at overload, part load and deep part load, reduce pressure pulsations and enlarge the know-how about the transient fluid flow through the turbine at these challenging conditions. Model test measurements showed that distinct runner designs were able to influence the pressure pulsation level in the machine. Extensive experimental investigations focused on the runner deflector geometry, on runner features and how they could reduce the pressure oscillation level. The impact of design variants and machine configurations on the vortex rope at the draft tube cone at overload and part load and on the runner channel vortex at deep part load were experimentally observed and evaluated based on the measured pressure pulsation amplitudes. Numerical investigations were employed for improving the understanding of such dynamic fluid flow effects. As example for the design and experimental investigations, model test observations and pressure pulsation curves for Francis machines in mid specific speed range, around n qopt = 50

Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model

International Nuclear Information System (INIS)

Wu, Jie; Wang, Jianzhou; Lu, Haiyan; Dong, Yao; Lu, Xiaoxiao

2013-01-01

Highlights: ► The seasonal and trend items of the data series are forecasted separately. ► Seasonal item in the data series is verified by the Kendall τ correlation testing. ► Different regression models are applied to the trend item forecasting. ► We examine the superiority of the combined models by the quartile value comparison. ► Paired-sample T test is utilized to confirm the superiority of the combined models. - Abstract: For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests

Analysis of Mesh Distribution Systems Considering Load Models and Load Growth Impact with Loops on System Performance

Science.gov (United States)

Kumar Sharma, A.; Murty, V. V. S. N.

2014-12-01

The distribution system is the final link between bulk power system and consumer end. A distinctive load flow solution method is used for analysis of the load flow of radial and weakly meshed network based on Kirchhoff's Current Law (KCL) and KVL. This method has excellent convergence characteristics for both radial as well as weakly meshed structure and is based on bus injection to branch current and branch-current to bus-voltage matrix. The main contribution of the paper is: (i) an analysis has been carried out for a weekly mesh network considering number of loops addition and its impact on the losses, kW and kVAr requirements from a system, and voltage profile, (ii) different load models, realistic ZIP load model and load growth impact on losses, voltage profile, kVA and kVAr requirements, (iii) impact of addition of loops on losses, voltage profile, kVA and kVAr requirements from substation, and (iv) comparison of system performance with radial distribution system. Voltage stability is a major concern in planning and operation of power systems. This paper also includes identifying the closeness critical bus which is the most sensitive to the voltage collapse in radial distribution networks. Node having minimum value of voltage stability index is the most sensitive node. Voltage stability index values are computed for meshed network with number of loops added in the system. The results have been obtained for IEEE 33 and 69 bus test system. The results have also been obtained for radial distribution system for comparison.

Blasting Vibration Safety Criterion Analysis with Equivalent Elastic Boundary: Based on Accurate Loading Model

Directory of Open Access Journals (Sweden)

Qingwen Li

2015-01-01

Full Text Available In the tunnel and underground space engineering, the blasting wave will attenuate from shock wave to stress wave to elastic seismic wave in the host rock. Also, the host rock will form crushed zone, fractured zone, and elastic seismic zone under the blasting loading and waves. In this paper, an accurate mathematical dynamic loading model was built. And the crushed zone as well as fractured zone was considered as the blasting vibration source thus deducting the partial energy for cutting host rock. So this complicated dynamic problem of segmented differential blasting was regarded as an equivalent elastic boundary problem by taking advantage of Saint-Venant’s Theorem. At last, a 3D model in finite element software FLAC3D accepted the constitutive parameters, uniformly distributed mutative loading, and the cylindrical attenuation law to predict the velocity curves and effective tensile curves for calculating safety criterion formulas of surrounding rock and tunnel liner after verifying well with the in situ monitoring data.

Musculoskeletal Modeling of a Forward Lunge Movement:Implications for ACL Loading

DEFF Research Database (Denmark)

Alkjaer, T; Wieland, MR; Andersen, MS

2010-01-01

are loaded during forward lunge? 2) Does the mechanical equilibrium cause ACL loads? Design: Computational modeling. Setting: The biomechanical forward lunge model was based on experimental motion capture data. Patients or Other Participants: One healthy female subject (height 5 169 cm, weight 5 59.6 kg, age....... The model and the pelvis. The hips were modeled as spherical joints, the knees as hinge joints, and the ankles as universal joints. Each according to a minimum fatigue criterion. Main Outcome Measures: Muscle and joint reaction forces that pulled the tibia in anterior or posterior direction. The forces were...... at the time of peak knee flexion. At peak knee flexion, the knee reaction was the only force that pulled the tibia anteriorly (2880 N). This was primarily counterbalanced by the musculus gluteus maximus (21940 N). Conclusions: The loading of the knee joint during lunging never required any stabilization...

Forecasting oil price trends using wavelets and hidden Markov models

International Nuclear Information System (INIS)

Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.

2010-01-01

The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

Science.gov (United States)

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

Application of Hierarchy Theory to Cross-Scale Hydrologic Modeling of Nutrient Loads

Science.gov (United States)

We describe a model called Regional Hydrologic Modeling for Environmental Evaluation 16 (RHyME2) for quantifying annual nutrient loads in stream networks and watersheds. RHyME2 is 17 a cross-scale statistical and process-based water-quality model. The model ...

101 Modelling and Forecasting Periodic Electric Load for a ...

African Journals Online (AJOL)

User

2012-01-24

Jan 24, 2012 ... Electricity load consumption in Nigeria is of great concern and its government is ... This is because the energy needed for any system is based on ... is a tool for verifying the validity and reliability of a chosen model. It tells how ...

Hierarchical HMM based learning of navigation primitives for cooperative robotic endovascular catheterization.

Science.gov (United States)

Rafii-Tari, Hedyeh; Liu, Jindong; Payne, Christopher J; Bicknell, Colin; Yang, Guang-Zhong

2014-01-01

Despite increased use of remote-controlled steerable catheter navigation systems for endovascular intervention, most current designs are based on master configurations which tend to alter natural operator tool interactions. This introduces problems to both ergonomics and shared human-robot control. This paper proposes a novel cooperative robotic catheterization system based on learning-from-demonstration. By encoding the higher-level structure of a catheterization task as a sequence of primitive motions, we demonstrate how to achieve prospective learning for complex tasks whilst incorporating subject-specific variations. A hierarchical Hidden Markov Model is used to model each movement primitive as well as their sequential relationship. This model is applied to generation of motion sequences, recognition of operator input, and prediction of future movements for the robot. The framework is validated by comparing catheter tip motions against the manual approach, showing significant improvements in the quality of catheterization. The results motivate the design of collaborative robotic systems that are intuitive to use, while reducing the cognitive workload of the operator.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

Science.gov (United States)

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Managing charging loads of electric vehicles by monetary incentives. A model-based optimization; Monetaere Anreize zur Steuerung der Ladelast von Elektrofahrzeugen. Eine modellgestuetzte Optimierung

Energy Technology Data Exchange (ETDEWEB)

Paetz, Alexandra-Gwyn; Kaschub, Thomas; Kopp, Martin; Jochem, Patrick; Fichtner, Wolf [Karlsruher Institut fuer Technologie, Karlsruhe (Germany). Inst. fuer Industriebetriebslehre und Industrielle Produktion

2013-03-15

Electric mobility is supposed to contribute to climate policy targets by reducing CO{sub 2}-emissions in the transportation sector. Increasing penetration rates of electric vehicles (EV) can lead to new challenges in the electricity sector, especially with regard to local distribution networks. Thus the management of charging loads is discussed as a key issue in energy economics. Due to their long parking times, high electricity and power demand, EV seem to be predestined for load management. Monetary incentives as dynamic pricing can be suitable for that: They reflect the current supply situation, pass the information to the consumers and can thus lead to a corresponding charging behaviour. In this article we analyse this interaction between dynamic pricing and charging loads. For this reason we have developed the optimization model DS-Opt+. It models a total number of 4,000 households in two residential areas of a major city with regard to its electricity demand, its mobility behaviour and its equipment of photovoltaic systems. Four different pricing models are tested for their effects on charging behaviour and thus the total load of the residential area. The results illustrate that only fairly high penetration rates of EV lead to remarkably higher electricity demand and require some load management. The tested dynamic pricing models are suitable for influencing charging loads; load-based tariffs are best in achieving a balanced load curve. In our analysis uncontrolled charging strategies are superior regarding a balanced load curve than controlled strategies by time-varying tariffs. Our results lead to several implications relevant for the energy industry and further research.

Load-redistribution strategy based on time-varying load against cascading failure of complex network

International Nuclear Information System (INIS)

Liu Jun; Shi Xin; Wang Kai; Shi Wei-Ren; Xiong Qing-Yu

2015-01-01

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently. (paper)

CFD-based design load analysis of 5MW offshore wind turbine

Science.gov (United States)

Tran, T. T.; Ryu, G. J.; Kim, Y. H.; Kim, D. H.

2012-11-01

The structure and aerodynamic loads acting on NREL 5MW reference wind turbine blade are calculated and analyzed based on advanced Computational Fluid Dynamics (CFD) and unsteady Blade Element Momentum (BEM). A detailed examination of the six force components has been carried out (three force components and three moment components). Structure load (gravity and inertia load) and aerodynamic load have been obtained by additional structural calculations (CFD or BEM, respectively,). In CFD method, the Reynolds Average Navier-Stokes approach was applied to solve the continuity equation of mass conservation and momentum balance so that the complex flow around wind turbines was modeled. Written in C programming language, a User Defined Function (UDF) code which defines transient velocity profile according to the Extreme Operating Gust condition was compiled into commercial FLUENT package. Furthermore, the unsteady BEM with 3D stall model has also adopted to investigate load components on wind turbine rotor. The present study introduces a comparison between advanced CFD and unsteady BEM for determining load on wind turbine rotor. Results indicate that there are good agreements between both present methods. It is importantly shown that six load components on wind turbine rotor is significant effect under Extreme Operating Gust (EOG) condition. Using advanced CFD and additional structural calculations, this study has succeeded to construct accuracy numerical methodology to estimate total load of wind turbine that compose of aerodynamic load and structure load.

Load function modelling for light impact

International Nuclear Information System (INIS)

Klingmueller, O.

1982-01-01

For Pile Integrity Testing light weight drop hammers are used to induce stress waves. In the computational analysis of one-dimensional wave propagation a load function has to be used. Several mechanical models and corresponding load functions are discussed. It is shown that a bell-shaped function which does not correspond to a mechanical model is in best accordance with test results and does not lead to numerical disturbances in the computational results. (orig.) [de

How uncertain is model-based prediction of copper loads in stormwater runoff?

DEFF Research Database (Denmark)

Lindblom, Erik Ulfson; Ahlman, S.; Mikkelsen, Peter Steen

2007-01-01

(runoff volumes and pollutant masses). We use the generalized likelihood uncertainty estimation (GLUE) methodology and generate posterior parameter distributions that result in model outputs encompassing a significant number of the highly variable measurements. Given the applied pollution accumulation......In this paper, we conduct a systematic analysis of the uncertainty related with estimating the total load of pollution (copper) from a separate stormwater drainage system, conditioned on a specific combination of input data, a dynamic conceptual pollutant accumulation-washout model and measurements...

An actual load forecasting methodology by interval grey modeling based on the fractional calculus.

Science.gov (United States)

Yang, Yang; Xue, Dingyü

2017-07-17

The operation processes for thermal power plant are measured by the real-time data, and a large number of historical interval data can be obtained from the dataset. Within defined periods of time, the interval information could provide important information for decision making and equipment maintenance. Actual load is one of the most important parameters, and the trends hidden in the historical data will show the overall operation status of the equipments. However, based on the interval grey parameter numbers, the modeling and prediction process is more complicated than the one with real numbers. In order not lose any information, the geometric coordinate features are used by the coordinates of area and middle point lines in this paper, which are proved with the same information as the original interval data. The grey prediction model for interval grey number by the fractional-order accumulation calculus is proposed. Compared with integer-order model, the proposed method could have more freedom with better performance for modeling and prediction, which can be widely used in the modeling process and prediction for the small amount interval historical industry sequence samples. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Load Composition Model Workflow (BPA TIP-371 Deliverable 1A)

Energy Technology Data Exchange (ETDEWEB)

Chassin, David P.; Cezar, Gustavo V.; /SLAC

2017-07-17

This project is funded under Bonneville Power Administration (BPA) Strategic Partnership Project (SPP) 17-005 between BPA and SLAC National Accelerator Laboratory. The project in a BPA Technology Improvement Project (TIP) that builds on and validates the Composite Load Model developed by the Western Electric Coordinating Council's (WECC) Load Modeling Task Force (LMTF). The composite load model is used by the WECC Modeling and Validation Work Group to study the stability and security of the western electricity interconnection. The work includes development of load composition data sets, collection of load disturbance data, and model development and validation. This work supports reliable and economic operation of the power system. This report was produced for Deliverable 1A of the BPA TIP-371 Project entitled \\TIP 371: Advancing the Load Composition Model". The deliverable documents the proposed work ow for the Composite Load Model, which provides the basis for the instrumentation, data acquisition, analysis and data dissemination activities addressed by later phases of the project.

Local load-sharing fiber bundle model in higher dimensions.

Science.gov (United States)

Sinha, Santanu; Kjellstadli, Jonas T; Hansen, Alex

2015-08-01

We consider the local load-sharing fiber bundle model in one to five dimensions. Depending on the breaking threshold distribution of the fibers, there is a transition where the fracture process becomes localized. In the localized phase, the model behaves as the invasion percolation model. The difference between the local load-sharing fiber bundle model and the equal load-sharing fiber bundle model vanishes with increasing dimensionality with the characteristics of a power law.

DYNAMIC LOAD DAMPER MODELING

Directory of Open Access Journals (Sweden)

Loktev Aleksey Alekseevich

2013-01-01

Full Text Available The authors present their findings associated with their modeling of a dynamic load damper. According to the authors, the damper is to be installed onto a structure or its element that may be exposed to impact, vibration or any other dynamic loading. The damper is composed of paralleled or consecutively connected viscous and elastic elements. The authors study the influence of viscosity and elasticity parameters of the damper produced onto the regular displacement of points of the structure to be protected and onto the regular acceleration transmitted immediately from the damper to the elements positioned below it.

Towards building a neural network model for predicting pile static load test curves

Directory of Open Access Journals (Sweden)

Alzo’ubi A. K.

2018-01-01

Full Text Available In the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behaviour, the Static Load Test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.

Testing and modeling of cyclically loaded rock anchors

Directory of Open Access Journals (Sweden)

Joar Tistel

2017-12-01

Full Text Available The Norwegian Public Roads Administration (NPRA is planning for an upgrade of the E39 highway route at the westcoast of Norway. Fixed links shall replace ferries at seven fjord crossings. Wide spans and large depths at the crossings combined with challenging subsea topography and environmental loads call for an extension of existing practice. A variety of bridge concepts are evaluated in the feasibility study. The structures will experience significant loads from deadweight, traffic and environment. Anchoring of these forces is thus one of the challenges met in the project. Large-size subsea rock anchors are considered a viable alternative. These can be used for anchoring of floating structures but also with the purpose of increasing capacity of fixed structures. This paper presents first a thorough study of factors affecting rock anchor bond capacity. Laboratory testing of rock anchors subjected to cyclic loading is thereafter presented. Finally, the paper presents a model predicting the capacity of a rock anchor segment, in terms of a ribbed bar, subjected to a cyclic load history. The research assumes a failure mode occurring in the interface between the rock anchor and the surrounding grout. The constitutive behavior of the bonding interface is investigated for anchors subjected to cyclic one-way tensile loads. The model utilizes the static bond capacity curve as a basis, defining the ultimate bond τbu and the slip s1 at τbu. A limited number of input parameters are required to apply the model. The model defines the bond-slip behavior with the belonging rock anchor capacity depending on the cyclic load level (τmax cy/τbu, the cyclic load ratio (R = τmin cy/τmax cy, and the number of load cycles (N. The constitutive model is intended to model short anchor lengths representing an incremental length of a complete rock anchor.

Hidden Markov modeling of frequency-following responses to Mandarin lexical tones.

Science.gov (United States)

Llanos, Fernando; Xie, Zilong; Chandrasekaran, Bharath

2017-11-01

The frequency-following response (FFR) is a scalp-recorded electrophysiological potential reflecting phase-locked activity from neural ensembles in the auditory system. The FFR is often used to assess the robustness of subcortical pitch processing. Due to low signal-to-noise ratio at the single-trial level, FFRs are typically averaged across thousands of stimulus repetitions. Prior work using this approach has shown that subcortical encoding of linguistically-relevant pitch patterns is modulated by long-term language experience. We examine the extent to which a machine learning approach using hidden Markov modeling (HMM) can be utilized to decode Mandarin tone-categories from scalp-record electrophysiolgical activity. We then assess the extent to which the HMM can capture biologically-relevant effects (language experience-driven plasticity). To this end, we recorded FFRs to four Mandarin tones from 14 adult native speakers of Chinese and 14 of native English. We trained a HMM to decode tone categories from the FFRs with varying size of averages. Tone categories were decoded with above-chance accuracies using HMM. The HMM derived metric (decoding accuracy) revealed a robust effect of language experience, such that FFRs from native Chinese speakers yielded greater accuracies than native English speakers. Critically, the language experience-driven plasticity was captured with average sizes significantly smaller than those used in the extant literature. Our results demonstrate the feasibility of HMM in assessing the robustness of neural pitch. Machine-learning approaches can complement extant analytical methods that capture auditory function and could reduce the number of trials needed to capture biological phenomena. Copyright © 2017 Elsevier B.V. All rights reserved.

Modelling of Vortex-Induced Loading on a Single-Blade Installation Setup

DEFF Research Database (Denmark)

Skrzypinski, Witold Robert; Gaunaa, Mac; Heinz, Joachim Christian

2016-01-01

the same procedure. The reference time series were significantly different, depending on the inflow angle. This made the modelling of all the time series with a single and relatively simple engineering model challenging. In order to find model parameters, optimizations were carried out, based on the root-mean-square......Vortex-induced integral loading fluctuations on a single suspended blade at various inflow angles were modeled in the presents work by means of stochastic modelling methods. The reference time series were obtained by 3D DES CFD computations carried out on the DTU 10MW reference wind turbine blade...... varied depending on the inflow angle. None the less, the modelled and reference time series showed a satisfactory general agreement in terms of their visual and frequency characteristics. This indicated that the proposed method is suitable to model loading fluctuations on suspended blades....

A tri-generation system based on polymer electrolyte fuel cell and desiccant wheel – Part A: Fuel cell system modelling and partial load analysis

International Nuclear Information System (INIS)

Najafi, Behzad; De Antonellis, Stefano; Intini, Manuel; Zago, Matteo; Rinaldi, Fabio; Casalegno, Andrea

2015-01-01

Highlights: • A mathematical model for a PEMFC based cogeneration system is developed. • Developed model is validated using the available experimental data. • Performance of the plant at full load conditions is investigated. • Performance indices while applying two different modifications are determined. • System’s performance with and without modifications at partial loads is investigated. - Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) based systems have recently received increasing attention as a viable alternative for meeting the residential electrical and thermal demands. However, as the intermittent demand profiles of a building can only be addressed by a tri-generative unit which can operate at partial loads, the variation of performance of the system at partial loads might affect its corresponding potential benefits significantly. Nonetheless, no previous study has been carried out on assessing the performance of this type of tri-generative systems in such conditions. The present paper is the first of a two part study dedicated to the investigation of the performance of a tri-generative system in which a PEMFC based system is coupled with a desiccant wheel unit. This study is focused on evaluating the performance of the PEMFC subsystem while operating at partial loads. Accordingly, a detailed mathematical model of the fuel cell subsystem is first developed and validated using the experimental data obtained from the plant’s and the fuel cell stack’s manufacturer. Next, in order to increase the performance of the plant, two modifications have been proposed and the resulting performance at partial load have been determined. The obtained results demonstrate that applying both modifications results in increasing the electrical efficiency of the plant by 5.5%. It is also shown that, while operating at partial loads, the electrical efficiency of the plant does not significantly change; the fact which corresponds to the trade-off between

Increasing precision of turbidity-based suspended sediment concentration and load estimates.

Science.gov (United States)

Jastram, John D; Zipper, Carl E; Zelazny, Lucian W; Hyer, Kenneth E

2010-01-01

Turbidity is an effective tool for estimating and monitoring suspended sediments in aquatic systems. Turbidity can be measured in situ remotely and at fine temporal scales as a surrogate for suspended sediment concentration (SSC), providing opportunity for a more complete record of SSC than is possible with physical sampling approaches. However, there is variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. This study investigated the potential to improve turbidity-based SSC, and by extension the resulting sediment loading estimates, by incorporating hydrologic variables that can be monitored remotely and continuously (typically 15-min intervals) into the SSC estimation procedure. On the Roanoke River in southwestern Virginia, hydrologic stage, turbidity, and other water-quality parameters were monitored with in situ instrumentation; suspended sediments were sampled manually during elevated turbidity events; samples were analyzed for SSC and physical properties including particle-size distribution and organic C content; and rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC estimation variance and hydrologic variables that explained variability of those physical properties. Results indicated that the inclusion of any of the measured physical properties in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables to represent these physical properties, along with turbidity, resulted in a model, relying solely on data collected remotely and continuously, that estimated SSC with less variance than a conventional turbidity-based univariate model, allowing a more precise estimate of sediment loading, Modeling results are consistent with known mechanisms governing sediment transport in hydrologic systems.

A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid

OpenAIRE

Ahmad, Ashfaq; Javaid, Nadeem; Alrajeh, Nabil; Khan, Zahoor; Qasim, Umar; Khan, Abid

2015-01-01

In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately ...

Capturing the state transitions of seizure-like events using Hidden Markov models.

Science.gov (United States)

Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2011-01-01

The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.

Eugenol-loaded chitosan nanoparticles: II. Application in bio-based plastics for active packaging.

Science.gov (United States)

Woranuch, Sarekha; Yoksan, Rangrong

2013-07-25

The aim of the present research was to study the possibility of using eugenol-loaded chitosan nanoparticles as antioxidants for active bio-based packaging material. Eugenol-loaded chitosan nanoparticles were incorporated into thermoplastic flour (TPF) - a model bio-based plastic - through an extrusion process at temperatures above 150°C. The influences of eugenol-loaded chitosan nanoparticles on crystallinity, morphology, thermal properties, radical scavenging activity, reducing power, tensile properties and barrier properties of TPF were investigated. Although the incorporation of 3% (w/w) of eugenol-loaded chitosan nanoparticles significantly reduced the extensibility and the oxygen barrier property of TPF, it provided antioxidant activity and improved the water vapor barrier property. In addition, TPF containing eugenol-loaded chitosan nanoparticles exhibited superior radical scavenging activity and stronger reducing power compared with TPF containing naked eugenol. The results suggest the applicability of TPF containing eugenol-loaded chitosan nanoparticles as an antioxidant active packaging material. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Dynamic Model for Load Balancing in Cloud Infrastructure

Directory of Open Access Journals (Sweden)

Jitendra Bhagwandas Bhatia

2015-08-01

Full Text Available This paper analysis various challenges faced in optimizing computing resource utilization via load balancing and presents a platform-independent model for load balancing which targets high availability of resources, low SLA (Service Level agreement violations and saves power. To achieve this, incoming requests are monitored for sudden burst, a prediction model is employed to maintain high availability and a power-aware algorithm is applied for choosing a suitable physical node for a virtual host. The proposed dynamic load balancing model provides a way to conflicting goals of saving power and maintaining high resource availability.For anyone building a private, public or hybrid IaaS cloud infrastructure, load balancing of virtual hosts on a limited number of physical nodes, becomes a crucial aspect. This paper analysis various challenges faced in optimizing computing resource utilization via load balancing and presents a platform independent model for load balancing which targets high availability of resources, low SLA (Service Level agreement violations and saves power. To achieve this, incoming requests are monitored for sudden burst, prediction model is employed to maintain high availability and power aware algorithm is applied for choosing a suitable physical node for virtual host. The proposed dynamic load balancing model provides a way to conflicting goals of saving power and maintaining high resource availability.

Mathematical modeling of deformation of reinforced femur during prolonged static loads

Directory of Open Access Journals (Sweden)

Vladimir P. Radchenko

2015-12-01

Full Text Available A two-layer mathematical model of a human femur neck reinforced implants of different design for modeling stress-strain state which occurs during a surgical procedure to prevent femur neck fractures by the forced introduction of metallic implants is proposed. Engineered implant designs are provided. Methods and software for geometric modeling of femur embedded with the implants are developed. New boundary value problems to evaluate kinetics in creep conditions of the stress-strain state of reinforced and non-reinforced femoral neck during prolonged static loads corresponding to human foot traffic are formulated. Effective elastic properties of cortical and cancellous bone, power and kinematic boundary value problems. A phenomenological creep model for compact bone tissue is constructed. The technique of identifying the parameters is developed. A check of its adequacy to experimental data is carried out. Based on the finite element method the numerical method for solving the provided boundary value problems at macro level of continuum mechanics is developed. A lot of variative calculations allowed developing recommendations for the rational positioning of the implant in order to minimize stress concentrations. The performed analysis showed that there is a significant relaxation of stresses in the most loaded areas due to creep. Relaxation is more intense in reinforced femoral neck than in the unreinforced. Thus the tension in the most loaded femoral neck area due to creep is reduced by 49 % with respect to the intensity of the initial time of loading for femur which is reinforced by the spoke-spoke-type implant when loading duration is 1 year under natural loads corresponding to human foot traffic. It was found that the time component (long-term fixed load does not impair the positive effect of reducing the stress concentration due to a femoral neck reinforcement which is a positive fact from the medical practice point of view.

Investigation into the use of water based brake fluid for light loads ...

African Journals Online (AJOL)

The actual test of the formulated brake fluid was carried out with a Nissan Sunny vehicle model 1.5 within the speed range of 20km/hr to 80km/hr at the permanent campus· of University of Uyo and the· braking effiqiency obtained at test to its suitability for light loads. Keywords·: Water-based, Brake fluid properties, Light loads ...

Metagenome and Metatranscriptome Analyses Using Protein Family Profiles.

Directory of Open Access Journals (Sweden)

Cuncong Zhong

2016-07-01

Full Text Available Analyses of metagenome data (MG and metatranscriptome data (MT are often challenged by a paucity of complete reference genome sequences and the uneven/low sequencing depth of the constituent organisms in the microbial community, which respectively limit the power of reference-based alignment and de novo sequence assembly. These limitations make accurate protein family classification and abundance estimation challenging, which in turn hamper downstream analyses such as abundance profiling of metabolic pathways, identification of differentially encoded/expressed genes, and de novo reconstruction of complete gene and protein sequences from the protein family of interest. The profile hidden Markov model (HMM framework enables the construction of very useful probabilistic models for protein families that allow for accurate modeling of position specific matches, insertions, and deletions. We present a novel homology detection algorithm that integrates banded Viterbi algorithm for profile HMM parsing with an iterative simultaneous alignment and assembly computational framework. The algorithm searches a given profile HMM of a protein family against a database of fragmentary MG/MT sequencing data and simultaneously assembles complete or near-complete gene and protein sequences of the protein family. The resulting program, HMM-GRASPx, demonstrates superior performance in aligning and assembling homologs when benchmarked on both simulated marine MG and real human saliva MG datasets. On real supragingival plaque and stool MG datasets that were generated from healthy individuals, HMM-GRASPx accurately estimates the abundances of the antimicrobial resistance (AMR gene families and enables accurate characterization of the resistome profiles of these microbial communities. For real human oral microbiome MT datasets, using the HMM-GRASPx estimated transcript abundances significantly improves detection of differentially expressed (DE genes. Finally, HMM

Efficient Blind System Identification of Non-Gaussian Auto-Regressive Models with HMM Modeling of the Excitation

DEFF Research Database (Denmark)

Li, Chunjian; Andersen, Søren Vang

2007-01-01

We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....

A new crack growth model for life prediction under random loading

International Nuclear Information System (INIS)

Lee, Ouk Sub; Chen, Zhi Wei

1999-01-01

The load interaction effect in variable amplitude fatigue test is a very important issue for correctly predicting fatigue life. Some prediction methods for retardation are reviewed and the problems discussed. The so-called 'under-load' effect is also of importance for a prediction model to work properly under random load spectrum. A new model that is simple in form but combines overload plastic zone and residual stress considerations together with Elber's closure concept is proposed to fully take account of the load-interaction effects including both over-load and under-load effects. Applying this new model to complex load sequence is explored here. Simulations of tests show the improvement of the new model over other models. The best prediction (mostly closely resembling test curve) is given by the newly proposed Chen-Lee model

Characteristics and modeling of spruce wood under dynamic compression load

International Nuclear Information System (INIS)

Eisenacher, Germar

2014-01-01

Spruce wood is frequently used as an energy absorbing material in impact limiters of packages for the transportation of radioactive material. A 9m drop test onto an unyielding target is mandatory for the packages. The impact results in a dynamic compression load of the spruce wood inside the impact limiter. The lateral dilation of the wood is restrained thereby due to encasing steel sheets. This work's objective was to provide a material model for spruce wood based on experimental investigations to enable the calculation of such loading conditions. About 600 crush tests with cubical spruce wood specimens were performed to characterize the material. The compression was up to 70% and the material was assumed to be transversely isotropic. Particularly the lateral constraint showed to have an important effect: the material develops a high lateral dilation without lateral constraint. The force-displacement characteristics show a comparably low force level and no or only slight hardening. Distinctive softening occurs after the linear-elastic region when loaded parallel to the fiber. On the other hand, using a lateral constraint results in significantly higher general force levels, distinctive hardening and lateral forces. The softening effect when loaded parallel to the fiber is less distinctive. Strain rate and temperature raise or lower the strength level, which was quantified for the applicable ranges of impact limiters. The hypothesis of an uncoupled evolution of the yield surface was proposed based on the experimental findings. It postulates an independent strength evolution with deviatoric and volumetric deformation. The hypothesis could be established using the first modeling approach, the modified LS-DYNA material model MAT075. A transversely isotropic material model was developed based thereupon and implemented in LS-DYNA. The material characteristics of spruce wood were considered using a multi-surface yield criterion and a non-associated flow rule. The yield

A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Weide Li

2017-05-01

Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

Optimization and modeling of the remote loading of luciferin into liposomes.

Science.gov (United States)

Hansen, Anders Højgaard; Lomholt, Michael A; Hansen, Per Lyngs; Mouritsen, Ole G; Arouri, Ahmad

2016-07-11

We carried out a mechanistic study to characterize and optimize the remote loading of luciferin into preformed liposomes of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine/1,2-dipalmitoyl-sn-glycero-3-phosphoglycerol (DPPC/DPPG) 7:3 mixtures. The influence of the loading agent (acetate, propionate, butyrate), the metal counterion (Na(+), K(+), Ca(+2), Mg(+2)), and the initial extra-liposomal amount of luciferin (nL(add)) on the luciferin Loading Efficiency (LE%) and luciferin-to-lipid weight ratio, i.e., Loading Capacity (LC), in the final formulation was determined. In addition, the effect of the loading process on the colloidal stability and phase behavior of the liposomes was monitored. Based on our experimental results, a theoretical model was developed to describe the course of luciferin remote loading. It was found that the highest luciferin loading was obtained with magnesium acetate. The use of longer aliphatic carboxylates or inorganic proton donors pronouncedly reduced luciferin loading, whereas the effect of the counterion was modest. The remote-loading process barely affected the colloidal stability and drug retention of the liposomes, albeit with moderate luciferin-induced membrane perturbations. The correlation between luciferin loading, expressed as LE% and LC, and nL(add) was established, and under our conditions the maximum LC was attained using an nL(add) of around 2.6μmol. Higher amounts of luciferin tend to pronouncedly perturb the liposome stability and luciferin retention. Our theoretical model furnishes a fair quantitative description of the correlation between nL(add) and luciferin loading, and a membrane permeability coefficient for uncharged luciferin of 1×10(-8)cm/s could be determined. We believe that our study will prove very useful to optimize the remote-loading strategies of moderately polar carboxylic acid drugs in general. Copyright © 2016 Elsevier B.V. All rights reserved.

The Inclusion of Arbitrary Load Histories in the Strength Decay Model for Stress Rupture

Science.gov (United States)

Reeder, James R.

2014-01-01

Stress rupture is a failure mechanism where failures can occur after a period of time, even though the material has seen no increase in load. Carbon/epoxy composite materials have demonstrated the stress rupture failure mechanism. In a previous work, a model was proposed for stress rupture of composite overwrap pressure vessels (COPVs) and similar composite structures based on strength degradation. However, the original model was limited to constant load periods (holds) at constant load. The model was expanded in this paper to address arbitrary loading histories and specifically the inclusions of ramp loadings up to holds and back down. The broadening of the model allows for failures on loading to be treated as any other failure that may occur during testing instead of having to be treated as a special case. The inclusion of ramps can also influence the length of the "safe period" following proof loading that was previously predicted by the model. No stress rupture failures are predicted in a safe period because time is required for strength to decay from above the proof level to the lower level of loading. Although the model can predict failures during the ramp periods, no closed-form solution for the failure times could be derived. Therefore, two suggested solution techniques were proposed. Finally, the model was used to design an experiment that could detect the difference between the strength decay model and a commonly used model for stress rupture. Although these types of models are necessary to help guide experiments for stress rupture, only experimental evidence will determine how well the model may predict actual material response. If the model can be shown to be accurate, current proof loading requirements may result in predicted safe periods as long as 10(13) years. COPVs design requirements for stress rupture may then be relaxed, allowing more efficient designs, while still maintaining an acceptable level of safety.

Hidden Markov models for labeled sequences

DEFF Research Database (Denmark)

Krogh, Anders Stærmose

1994-01-01

A hidden Markov model for labeled observations, called a class HMM, is introduced and a maximum likelihood method is developed for estimating the parameters of the model. Instead of training it to model the statistics of the training sequences it is trained to optimize recognition. It resembles MMI...

Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.

Science.gov (United States)

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-02-15

Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.

Model-based energy monitoring and diagnosis of telecommunication cooling systems

International Nuclear Information System (INIS)

Sorrentino, Marco; Acconcia, Matteo; Panagrosso, Davide; Trifirò, Alena

2016-01-01

A methodology is proposed for on-line monitoring of cooling load supplied by Telecommunication (TLC) cooling systems. Sensible cooling load is estimated via a proportional integral controller-based input estimator, whereas a lumped parameters model was developed aiming at estimating air handling units (AHUs) latent heat load removal. The joint deployment of above estimators enables accurate prediction of total cooling load, as well as of related AHUs and free-coolers energy performance. The procedure was then proven effective when extended to cooling systems having a centralized chiller, through model-based estimation of a key performance metric, such as the energy efficiency ratio. The results and experimental validation presented throughout the paper confirm the suitability of the proposed procedure as a reliable and effective energy monitoring and diagnostic tool for TLC applications. Moreover, the proposed modeling approach, beyond its direct contribution towards smart use and conservation of energy, can be fruitfully deployed as a virtual sensor of removed heat load into a variety of residential and industrial applications. - Highlights: • Accurate cooling load prediction in telecommunication rooms. • Development of an input-estimator for sensible cooling load simulation. • Model-based estimation of latent cooling load. • Model-based prediction of centralized chiller energy performance in central offices. • Diagnosis-oriented application of proposed cooling load estimator.

CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information

DEFF Research Database (Denmark)

Bartoli, Lisa; Fariselli, Piero; Krogh, Anders

2009-01-01

tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods. RESULTS: In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence...... alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches...

Bayesian Mixed Hidden Markov Models: A Multi-Level Approach to Modeling Categorical Outcomes with Differential Misclassification

Science.gov (United States)

Zhang, Yue; Berhane, Kiros

2014-01-01

Questionnaire-based health status outcomes are often prone to misclassification. When studying the effect of risk factors on such outcomes, ignoring any potential misclassification may lead to biased effect estimates. Analytical challenges posed by these misclassified outcomes are further complicated when simultaneously exploring factors for both the misclassification and health processes in a multi-level setting. To address these challenges, we propose a fully Bayesian Mixed Hidden Markov Model (BMHMM) for handling differential misclassification in categorical outcomes in a multi-level setting. The BMHMM generalizes the traditional Hidden Markov Model (HMM) by introducing random effects into three sets of HMM parameters for joint estimation of the prevalence, transition and misclassification probabilities. This formulation not only allows joint estimation of all three sets of parameters, but also accounts for cluster level heterogeneity based on a multi-level model structure. Using this novel approach, both the true health status prevalence and the transition probabilities between the health states during follow-up are modeled as functions of covariates. The observed, possibly misclassified, health states are related to the true, but unobserved, health states and covariates. Results from simulation studies are presented to validate the estimation procedure, to show the computational efficiency due to the Bayesian approach and also to illustrate the gains from the proposed method compared to existing methods that ignore outcome misclassification and cluster level heterogeneity. We apply the proposed method to examine the risk factors for both asthma transition and misclassification in the Southern California Children's Health Study (CHS). PMID:24254432

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Directory of Open Access Journals (Sweden)

Yunxuan Dong

2017-04-01

Full Text Available The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

Thoracolumbar spine model with articulated ribcage for the prediction of dynamic spinal loading.

Science.gov (United States)

Ignasiak, Dominika; Dendorfer, Sebastian; Ferguson, Stephen J

2016-04-11

Musculoskeletal modeling offers an invaluable insight into the spine biomechanics. A better understanding of thoracic spine kinetics is essential for understanding disease processes and developing new prevention and treatment methods. Current models of the thoracic region are not designed for segmental load estimation, or do not include the complex construct of the ribcage, despite its potentially important role in load transmission. In this paper, we describe a numerical musculoskeletal model of the thoracolumbar spine with articulated ribcage, modeled as a system of individual vertebral segments, elastic elements and thoracic muscles, based on a previously established lumbar spine model and data from the literature. The inverse dynamics simulations of the model allow the prediction of spinal loading as well as costal joints kinetics and kinematics. The intradiscal pressure predicted by the model correlated well (R(2)=0.89) with reported intradiscal pressure measurements, providing a first validation of the model. The inclusion of the ribcage did not affect segmental force predictions when the thoracic spine did not perform motion. During thoracic motion tasks, the ribcage had an important influence on the predicted compressive forces and muscle activation patterns. The compressive forces were reduced by up to 32%, or distributed more evenly between thoracic vertebrae, when compared to the predictions of the model without ribcage, for mild thoracic flexion and hyperextension tasks, respectively. The presented musculoskeletal model provides a tool for investigating thoracic spine loading and load sharing between vertebral column and ribcage during dynamic activities. Further validation for specific applications is still necessary. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modelling the pile load test

OpenAIRE

Prekop Ľubomír

2017-01-01

This paper deals with the modelling of the load test of horizontal resistance of reinforced concrete piles. The pile belongs to group of piles with reinforced concrete heads. The head is pressed with steel arches of a bridge on motorway D1 Jablonov - Studenec. Pile model was created in ANSYS with several models of foundation having properties found out from geotechnical survey. Finally some crucial results obtained from computer models are presented and compared with these obtained from exper...

Continuum topology optimization considering uncertainties in load locations based on the cloud model

Science.gov (United States)

Liu, Jie; Wen, Guilin

2018-06-01

Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.

Dual Sticky Hierarchical Dirichlet Process Hidden Markov Model and Its Application to Natural Language Description of Motions.

Science.gov (United States)

Hu, Weiming; Tian, Guodong; Kang, Yongxin; Yuan, Chunfeng; Maybank, Stephen

2017-09-25

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov model (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequence of atomic activities, the action represented by the trajectory can be described in natural language in as automatic a way as possible. The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene.

Performance assessment of a power loaded supercapacitor based on manufacturer data

International Nuclear Information System (INIS)

Mellincovsky, Martin; Kuperman, Alon; Lerman, Chaim; Aharon, Ilan; Reichbach, Noam; Geula, Gal; Nakash, Ronen

2013-01-01

Highlights: • Analytic performance of a power loaded supercapacitor is derived. • Power and energy capabilities based on manufacturer data are obtained. • Power limitations based on depth of discharge are presented. - Abstract: Analytical derivation of constant power loaded supercapacitor behavior is presented in the paper. Simple RC model based on manufacturer datasheet extracted parameters is employed. Power and energy related figures of merit are obtained from the derived expressions and compared to the datasheet provided values. It is revealed that some of the performance characteristics provided in most of the datasheets are theoretical and cannot be achieved in practice. The process of a realistic Ragone plot derivation based on the proposed method is described in the paper as well. It is shown that the lower limit of supercapacitor voltage imposes certain limits on power and energy capabilities of the device. Extended simulation and experimental results are provided in order to reinforce the proposed method and justify the selected RC model for describing the supercapacitor performance. By appropriate comparison of simulations and experiments it is proven that the selected model, while being oversimplified and low order, may be used to predict supercapacitor behavior with reasonable accuracy to perform at least an initial design

Energy Savings in Cellular Networks Based on Space-Time Structure of Traffic Loads

Science.gov (United States)

Sun, Jingbo; Wang, Yue; Yuan, Jian; Shan, Xiuming

Since most of energy consumed by the telecommunication infrastructure is due to the Base Transceiver Station (BTS), switching off BTSs when traffic load is low has been recognized as an effective way of saving energy. In this letter, an energy saving scheme is proposed to minimize the number of active BTSs based on the space-time structure of traffic loads as determined by principal component analysis. Compared to existing methods, our approach models traffic loads more accurately, and has a much smaller input size. As it is implemented in an off-line manner, our scheme also avoids excessive communications and computing overheads. Simulation results show that the proposed method has a comparable performance in energy savings.

A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions.

Science.gov (United States)

Zheng, Yang; Zhou, Jianzhong; Xu, Yanhe; Zhang, Yuncheng; Qian, Zhongdong

2017-05-01

This paper proposes a distributed model predictive control based load frequency control (MPC-LFC) scheme to improve control performances in the frequency regulation of power system. In order to reduce the computational burden in the rolling optimization with a sufficiently large prediction horizon, the orthonormal Laguerre functions are utilized to approximate the predicted control trajectory. The closed-loop stability of the proposed MPC scheme is achieved by adding a terminal equality constraint to the online quadratic optimization and taking the cost function as the Lyapunov function. Furthermore, the treatments of some typical constraints in load frequency control have been studied based on the specific Laguerre-based formulations. Simulations have been conducted in two different interconnected power systems to validate the effectiveness of the proposed distributed MPC-LFC as well as its superiority over the comparative methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Validation of the Dynamic Wake Meander model with focus on tower loads

Science.gov (United States)

Larsen, T. J.; Larsen, G. C.; Pedersen, M. M.; Enevoldsen, K.; Madsen, H. A.

2017-05-01

This paper presents a comparison between measured and simulated tower loads for the Danish offshore wind farm Nysted 2. Previously, only limited full scale experimental data containing tower load measurements have been published, and in many cases the measurements include only a limited range of wind speeds. In general, tower loads in wake conditions are very challenging to predict correctly in simulations. The Nysted project offers an improved insight to this field as six wind turbines located in the Nysted II wind farm have been instrumented to measure tower top and tower bottom moments. All recorded structural data have been organized in a database, which in addition contains relevant wind turbine SCADA data as well as relevant meteorological data - e.g. wind speed and wind direction - from an offshore mast located in the immediate vicinity of the wind farm. The database contains data from a period extending over a time span of more than 3 years. Based on the recorded data basic mechanisms driving the increased loading experienced by wind turbines operating in offshore wind farm conditions have been identified, characterized and modeled. The modeling is based on the Dynamic Wake Meandering (DWM) approach in combination with the state-of-the-art aeroelastic model HAWC2, and has previously as well as in this study shown good agreement with the measurements. The conclusions from the study have several parts. In general the tower bending and yaw loads show a good agreement between measurements and simulations. However, there are situations that are still difficult to match. One is tower loads of single-wake operation near rated ambient wind speed for single wake situations for spacing’s around 7-8D. A specific target of the study was to investigate whether the largest tower fatigue loads are associated with a certain downstream distance. This has been identified in both simulations and measurements, though a rather flat optimum is seen in the measurements.

Investigation into the Use of Water Based Brake Fluid for Light Loads

Directory of Open Access Journals (Sweden)

W. A. Akpan

2012-12-01

Full Text Available This paper addresses the possibility of using water based fluid as a brake fluid for light loads. Characterization of both standard and water based braked fluids formulated was carried out. The properties of the latter were compared with that of a standard commercial brake fluid. The actual test of the formulated brake fluid was carried out with a Nissan Sunny vehicle model 1.5 within the speed range of 20km/hr to 80km/hr at the permanent campus of University of Uyo and the braking efficiency obtained attest to its suitability for light loads.

A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

Directory of Open Access Journals (Sweden)

H. Othman

2007-02-01

Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.

A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

Directory of Open Access Journals (Sweden)

Othman H

2007-01-01

Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.

Modelling the pile load test

Directory of Open Access Journals (Sweden)

Prekop Ľubomír

2017-01-01

Full Text Available This paper deals with the modelling of the load test of horizontal resistance of reinforced concrete piles. The pile belongs to group of piles with reinforced concrete heads. The head is pressed with steel arches of a bridge on motorway D1 Jablonov - Studenec. Pile model was created in ANSYS with several models of foundation having properties found out from geotechnical survey. Finally some crucial results obtained from computer models are presented and compared with these obtained from experiment.

Modeling of high-strength concrete-filled FRP tube columns under cyclic load

Science.gov (United States)

Ong, Kee-Yen; Ma, Chau-Khun; Apandi, Nazirah Mohd; Awang, Abdullah Zawawi; Omar, Wahid

2018-05-01

The behavior of high-strength concrete (HSC) - filled fiber-reinforced-polymer (FRP) tubes (HSCFFTs) column subjected to cyclic lateral loading is presented in this paper. As the experimental study is costly and time consuming, a finite element analysis (FEA) is chosen for the study. Most of the previous studies have focused on examining the axial load behavior of HSCFFT column instead of seismic behavior. The seismic behavior of HSCFFT columns has been the main interest in the industry. The key objective of this research is to develop a reliable numerical non-linear FEA model to represent the seismic behavior of such column. A FEA model was developed using the Concrete Damaged Plasticity Model (CDPM) available in the finite element software package (ABAQUS). Comparisons between experimental results from previous research and the predicted results were made based on load versus displacement relationships and ultimate strength of the column. The results showed that the column increased in ductility and able to deform to a greater extent with the increase of the FRP confinement ratio. With the increase of confinement ratio, HSCFFT column achieved a higher moment resistance, thus indicated a higher failure strength in the column under cyclic lateral load. It was found that the proposed FEA model can regenerate the experimental results with adequate accuracy.

Drilling Load Model of an Inchworm Boring Robot for Lunar Subsurface Exploration

Directory of Open Access Journals (Sweden)

Weiwei Zhang

2017-01-01

Full Text Available In the past decade, the wireline robot has received increasing attention due to the advantages of light weight, low cost, and flexibility compared to the traditional drilling instruments in space missions. For the lunar subsurface in situ exploration mission, we proposed a type of wireline robot named IBR (Inchworm Boring Robot drawing inspiration from the inchworm. Two auger tools are utilized to remove chips for IBR, which directly interacted with the lunar regolith in the drilling process. Therefore, for obtaining the tools drilling characteristics, the chips removal principle of IBR is analyzed and its drilling load model is further established based on the soil mechanical theory in this paper. And then the proposed theoretical drilling load model is experimentally validated. In addition, according to the theoretical drilling load model, this paper discusses the effect of the drilling parameters on the tools drilling moments and power consumption. These results imply a possible energy-efficient control strategy for IBR.

Analytical Model of Coil Spring Damper Based on the Loading Test

Energy Technology Data Exchange (ETDEWEB)

Cho, Sung Gook; Park, Woong Ki [INNOSE TECH Co. LTD, Incheon (Korea, Republic of); Furuya, Osamu [Tokyo City University, Tokyo (Japan); Kurabayashi, Hiroshi [Vibro-System, Tokyo (Japan)

2016-05-15

The one way of solving such problems is to enhance and to develop an improved damping element used in base-isolation and response control system. A cost reduction of damper for a large scale structure is another important task to upgrade the total response control abilities in the near future. This study has examined a response control device using elastoplastic hysteresis damping of metal material. The proposed damper is designed to be coil spring element shape for a uniform stress of metal and for a reduction of low cyclic fatigue in large deformation to upgrade a repetitive strength during the earthquake motions. By using the metal material of SS400 general structural rolled steel, the corresponding cost issues of the damping element will be effectively reduced. The analytical of elasto-plastic coil spring damper (CSD) is introduced, and basic mechanical properties evaluated experimentally and analytically. This study has been examined the response control damper using elasto-plastic hysteresis characteristics of metal material. The paper described the design method of elasto-plastic coil spring damper, basic mechanical properties evaluated from loading test, and analytical model of damper are summarized. It was confirmed that the damping force and mechanical characteristics of elasto-plastic coil spring damper are almost satisfied the design specifications.

Sensitivity kernels for viscoelastic loading based on adjoint methods

Science.gov (United States)

Al-Attar, David; Tromp, Jeroen

2014-01-01

Observations of glacial isostatic adjustment (GIA) allow for inferences to be made about mantle viscosity, ice sheet history and other related parameters. Typically, this inverse problem can be formulated as minimizing the misfit between the given observations and a corresponding set of synthetic data. When the number of parameters is large, solution of such optimization problems can be computationally challenging. A practical, albeit non-ideal, solution is to use gradient-based optimization. Although the gradient of the misfit required in such methods could be calculated approximately using finite differences, the necessary computation time grows linearly with the number of model parameters, and so this is often infeasible. A far better approach is to apply the `adjoint method', which allows the exact gradient to be calculated from a single solution of the forward problem, along with one solution of the associated adjoint problem. As a first step towards applying the adjoint method to the GIA inverse problem, we consider its application to a simpler viscoelastic loading problem in which gravitationally self-consistent ocean loading is neglected. The earth model considered is non-rotating, self-gravitating, compressible, hydrostatically pre-stressed, laterally heterogeneous and possesses a Maxwell solid rheology. We determine adjoint equations and Fréchet kernels for this problem based on a Lagrange multiplier method. Given an objective functional J defined in terms of the surface deformation fields, we show that its first-order perturbation can be written δ J = int _{MS}K_{η }δ ln η dV +int _{t0}^{t1}int _{partial M}K_{dot{σ }} δ dot{σ } dS dt, where δ ln η = δη/η denotes relative viscosity variations in solid regions MS, dV is the volume element, δ dot{σ } is the perturbation to the time derivative of the surface load which is defined on the earth model's surface ∂M and for times [t0, t1] and dS is the surface element on ∂M. The `viscosity

An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring

International Nuclear Information System (INIS)

Giri, Suman; Bergés, Mario

2015-01-01

Highlights: • Energy estimation is NILM has not yet accounted for complexity of appliance models. • We present a data-driven framework for appliance modeling in supervised NILM. • We test the framework on 3 houses and report average accuracies of 5.9–22.4%. • Appliance models facilitate the estimation of energy consumed by the appliance. - Abstract: Non-Intrusive Load Monitoring (NILM) is a set of techniques used to estimate the electricity consumed by individual appliances in a building from measurements of the total electrical consumption. Most commonly, NILM works by first attributing any significant change in the total power consumption (also known as an event) to a specific load and subsequently using these attributions (i.e. the labels for the events) to estimate energy for each load. For this last step, most published work in the field makes simplifying assumptions to make the problem more tractable. In this paper, we present a framework for creating appliance models based on classification labels and aggregate power measurements that can help to relax many of these assumptions. Our framework automatically builds models for appliances to perform energy estimation. The model relies on feature extraction, clustering via affinity propagation, perturbation of extracted states to ensure that they mimic appliance behavior, creation of finite state models, correction of any errors in classification that might violate the model, and estimation of energy based on corrected labels. We evaluate our framework on 3 houses from standard datasets in the field and show that the framework can learn data-driven models based on event labels and use that to estimate energy with lower error margins (e.g., 1.1–42.3%) than when using the heuristic models used by others

Waste Load Allocation Based on Total Maximum Daily Load Approach Using the Charged System Search (CSS Algorithm

Directory of Open Access Journals (Sweden)

Elham Faraji

2016-03-01

Full Text Available In this research, the capability of a charged system search algorithm (CSS in handling water management optimization problems is investigated. First, two complex mathematical problems are solved by CSS and the results are compared with those obtained from other metaheuristic algorithms. In the last step, the optimization model developed by the CSS algorithm is applied to the waste load allocation in rivers based on the total maximum daily load (TMDL concept. The results are presented in Tables and Figures for easy comparison. The study indicates the superiority of the CSS algorithm in terms of its speed and performance over the other metaheuristic algorithms while its precision in water management optimization problems is verified.

Analytic model for ultrasound energy receivers and their optimal electric loads II: Experimental validation

Science.gov (United States)

Gorostiaga, M.; Wapler, M. C.; Wallrabe, U.

2017-10-01

In this paper, we verify the two optimal electric load concepts based on the zero reflection condition and on the power maximization approach for ultrasound energy receivers. We test a high loss 1-3 composite transducer, and find that the measurements agree very well with the predictions of the analytic model for plate transducers that we have developed previously. Additionally, we also confirm that the power maximization and zero reflection loads are very different when the losses in the receiver are high. Finally, we compare the optimal load predictions by the KLM and the analytic models with frequency dependent attenuation to evaluate the influence of the viscosity.

Partially Hidden Markov Models

DEFF Research Database (Denmark)

Forchhammer, Søren Otto; Rissanen, Jorma

1996-01-01

Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where...

Optical character recognition of handwritten Arabic using hidden Markov models

Science.gov (United States)

Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

2011-04-01

The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

Modeling the effects of perceptual load: saliency, competitive interactions, and top-down biases.

Directory of Open Access Journals (Sweden)

Kleanthis eNeokleous

2016-01-01

Full Text Available A computational model of visual selective attention has been implemented to account for experimental findings on the Perceptual Load Theory (PLT of attention. The model was designed based on existing neurophysiological findings on attentional processes with the objective to offer an explicit and biologically plausible formulation of PLT. Simulation results verified that the proposed model is capable of capturing the basic pattern of results that support the Perceptual Load Theory as well as findings that are considered contradictory to the theory. Importantly, the model is able to reproduce the behavioral results from a dilution experiment, providing thus a way to reconcile PLT with the competing Dilution account. Overall, the model presents a novel account for explaining PLT effects on the basis of the low-level competitive interactions among neurons that represent visual input and the top-down signals that modulate neural activity. The implications of the model concerning the debate on the locus of selective attention as well as the origins of distractor interference in visual displays of varying load are discussed.

Helicopter Rotor Load Prediction Using a Geometrically Exact Beam with Multicomponent Model

DEFF Research Database (Denmark)

Lee, Hyun-Ku; Viswamurthy, S.R.; Park, Sang Chul

2010-01-01

In this paper, an accurate structural dynamic analysis was developed for a helicopter rotor system including rotor control components, which was coupled to various aerodynamic and wake models in order to predict an aeroelastic response and the loads acting on the rotor. Its blade analysis was based...... rotor-blade/control-system model was loosely coupled with various inflow and wake models in order to simulate both hover and forward-flight conditions. The resulting rotor blade response and pitch link loads are in good agreement with those predicted byCAMRADII. The present analysis features both model...... on an intrinsic formulation of moving beams implemented in the time domain. The rotor control system was modeled as a combination of rigid and elastic components. A multicomponent analysis was then developed by coupling the beam finite element model with the rotor control system model to obtain a complete rotor-blade/control...

Lumbopelvic muscle activation patterns in three stances under graded loading conditions: Proposing a tensegrity model for load transfer through the sacroiliac joints.

Science.gov (United States)

Pardehshenas, Hamed; Maroufi, Nader; Sanjari, Mohammad Ali; Parnianpour, Mohamad; Levin, Stephen M

2014-10-01

According to the conventional arch model of the pelvis, stability of the sacroiliac joints may require a predominance of form and force closure mechanisms: the greater the vertical shear force at the sacroiliac joints, the greater the reliance on self-bracing by horizontally or obliquely oriented muscles (such as the internal oblique). But what happens to the arch model when a person stands on one leg? In such cases, the pelvis no longer has imposts, leaving both the arch, and the arch model theory, without support. Do lumbopelvic muscle activation patterns in one-legged stances under load suggest compatibility with a different model? This study compares lumbopelvic muscle activation patterns in two-legged and one-legged stances in response to four levels of graded trunk loading in order to further our understanding the stabilization of the sacroiliac joints. Thirty male subjects experienced four levels of trunk loading (0%, 5%, 10% and 15% of body weight) by holding a bucket at one side, at three conditions: 1) two-legged standing with the bucket in the dominant hand, 2) ipsilateral loading: one-legged standing with the bucket in the dominant hand while using the same-side leg, and 3) contralateral loading: one-legged standing using the same leg used in condition 2, but with the bucket in the non-dominant hand. During these tasks, EMG signals from eight lumbopelvic muscles were collected. ANOVA with repeated design was performed on normalized EMG's to test the main effect of load and condition, and interaction effects of load by condition. Latissimus dorsi and erector spinae muscles showed an antagonistic pattern of activity toward the direction of load which may suggest these muscles as lateral trunk stabilizers. Internal oblique muscles showed a co-activation pattern with increasing task demand, which may function to increase lumbopelvic stability (P sacroiliac joint dysfunctions must be taken into consideration. Our hypothetical model may initiate thinking and

Application of fuzzy – Neuro to model weather parameter variability impacts on electrical load based on long-term forecasting

Directory of Open Access Journals (Sweden)

Danladi Ali

2018-03-01

Full Text Available Long-term load forecasting provides vital information about future load and it helps the power industries to make decision regarding electrical energy generation and delivery. In this work, fuzzy – neuro model is developed to forecast a year ahead load in relation to weather parameter (temperature and humidity in Mubi, Adamawa State. It is observed that: electrical load increased with increase in temperature and relative humidity does not show notable effect on electrical load. The accuracy of the prediction is obtained at 98.78% with the corresponding mean absolute percentage error (MAPE of 1.22%. This confirms that fuzzy – neuro is a good tool for load forecasting. Keywords: Electrical load, Load forecasting, Fuzzy logic, Back propagation, Neuro-fuzzy, Weather parameter

Self-organized dynamics in local load-sharing fiber bundle models.

Science.gov (United States)

Biswas, Soumyajyoti; Chakrabarti, Bikas K

2013-10-01

We study the dynamics of a local load-sharing fiber bundle model in two dimensions under an external load (which increases with time at a fixed slow rate) applied at a single point. Due to the local load-sharing nature, the redistributed load remains localized along the boundary of the broken patch. The system then goes to a self-organized state with a stationary average value of load per fiber along the (increasing) boundary of the broken patch (damaged region) and a scale-free distribution of avalanche sizes and other related quantities are observed. In particular, when the load redistribution is only among nearest surviving fiber(s), the numerical estimates of the exponent values are comparable with those of the Manna model. When the load redistribution is uniform along the patch boundary, the model shows a simple mean-field limit of this self-organizing critical behavior, for which we give analytical estimates of the saturation load per fiber values and avalanche size distribution exponent. These are in good agreement with numerical simulation results.

Wind Turbine Load Mitigation based on Multivariable Robust Control and Blade Root Sensors

Science.gov (United States)

Díaz de Corcuera, A.; Pujana-Arrese, A.; Ezquerra, J. M.; Segurola, E.; Landaluze, J.

2014-12-01

This paper presents two H∞ multivariable robust controllers based on blade root sensors' information for individual pitch angle control. The wind turbine of 5 MW defined in the Upwind European project is the reference non-linear model used in this research work, which has been modelled in the GH Bladed 4.0 software package. The main objective of these controllers is load mitigation in different components of wind turbines during power production in the above rated control zone. The first proposed multi-input multi-output (MIMO) individual pitch H" controller mitigates the wind effect on the tower side-to-side acceleration and reduces the asymmetrical loads which appear in the rotor due to its misalignment. The second individual pitch H" multivariable controller mitigates the loads on the three blades reducing the wind effect on the bending flapwise and edgewise momentums in the blades. The designed H" controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases.

Biomechanical Modeling Analysis of Loads Configuration for Squat Exercise

Science.gov (United States)

Gallo, Christopher A.; Thompson, William K.; Lewandowski, Beth E.; Jagodnik, Kathleen; De Witt, John K.

2017-01-01

INTRODUCTION: Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to assist loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft for travel to the Moon or to Mars is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and the musculoskeletal forces that occur during exercises performed on the prototype devices. METHODS The computational models currently under development utilize the OpenSim [1] software platform, consisting of open source code for musculoskeletal modeling, using biomechanical input data from test subjects for estimation of muscle and joint loads. The OpenSim Full Body Model [2] is used for all analyses. The model incorporates simplified wrap surfaces, a new knee model and updated lower body muscle parameters derived from cadaver measurements and magnetic resonance imaging of young adults. The upper body uses torque actuators at the lumbar and extremity joints. The test subjects who volunteer for this study are instrumented with reflective markers for motion capture data collection while performing squat exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device (ZIN Technologies, Middleburg Heights, OH). Ground reaction force data is collected with force plates under the feet, and device loading is recorded through load cells internal to the HULK. Test variables include

Data to support statistical modeling of instream nutrient load based on watershed attributes, southeastern United States, 2002

Science.gov (United States)

Hoos, Anne B.; Terziotti, Silvia; McMahon, Gerard; Savvas, Katerina; Tighe, Kirsten C.; Alkons-Wolinsky, Ruth

2008-01-01

This report presents and describes the digital datasets that characterize nutrient source inputs, environmental characteristics, and instream nutrient loads for the purpose of calibrating and applying a nutrient water-quality model for the southeastern United States for 2002. The model area includes all of the river basins draining to the south Atlantic and the eastern Gulf of Mexico, as well as the Tennessee River basin (referred to collectively as the SAGT area). The water-quality model SPARROW (SPAtially-Referenced Regression On Watershed attributes), developed by the U.S. Geological Survey, uses a regression equation to describe the relation between watershed attributes (predictors) and measured instream loads (response). Watershed attributes that are considered to describe nutrient input conditions and are tested in the SPARROW model for the SAGT area as source variables include atmospheric deposition, fertilizer application to farmland, manure from livestock production, permitted wastewater discharge, and land cover. Watershed and channel attributes that are considered to affect rates of nutrient transport from land to water and are tested in the SAGT SPARROW model as nutrient-transport variables include characteristics of soil, landform, climate, reach time of travel, and reservoir hydraulic loading. Datasets with estimates of each of these attributes for each individual reach or catchment in the reach-catchment network are presented in this report, along with descriptions of methods used to produce them. Measurements of nutrient water quality at stream monitoring sites from a combination of monitoring programs were used to develop observations of the response variable - mean annual nitrogen or phosphorus load - in the SPARROW regression equation. Instream load of nitrogen and phosphorus was estimated using bias-corrected log-linear regression models using the program Fluxmaster, which provides temporally detrended estimates of long-term mean load well

Response Load Extrapolation for Wind Turbines during Operation Based on Average Conditional Exceedance Rates

DEFF Research Database (Denmark)

Toft, Henrik Stensgaard; Naess, Arvid; Saha, Nilanjan

2011-01-01

to cases where the Gumbel distribution is the appropriate asymptotic extreme value distribution. However, two extra parameters are introduced by which a more general and flexible class of extreme value distributions is obtained with the Gumbel distribution as a subclass. The general method is implemented...... within a hierarchical model where the variables that influence the loading are divided into ergodic variables and time-invariant non-ergodic variables. The presented method for statistical response load extrapolation was compared with the existing methods based on peak extrapolation for the blade out......The paper explores a recently developed method for statistical response load (load effect) extrapolation for application to extreme response of wind turbines during operation. The extrapolation method is based on average conditional exceedance rates and is in the present implementation restricted...

Evaluation of soft segment modeling on a context independent phoneme classification system

International Nuclear Information System (INIS)

Razzazi, F.; Sayadiyan, A.

2007-01-01

The geometric distribution of states duration is one of the main performance limiting assumptions of hidden Markov modeling of speech signals. Stochastic segment models, generally, and segmental HMM, specifically overcome this deficiency partly at the cost of more complexity in both training and recognition phases. In addition to this assumption, the gradual temporal changes of speech statistics has not been modeled in HMM. In this paper, a new duration modeling approach is presented. The main idea of the model is to consider the effect of adjacent segments on the probability density function estimation and evaluation of each acoustic segment. This idea not only makes the model robust against segmentation errors, but also it models gradual change from one segment to the next one with a minimum set of parameters. The proposed idea is analytically formulated and tested on a TIMIT based context independent phenomena classification system. During the test procedure, the phoneme classification of different phoneme classes was performed by applying various proposed recognition algorithms. The system was optimized and the results have been compared with a continuous density hidden Markov model (CDHMM) with similar computational complexity. The results show 8-10% improvement in phoneme recognition rate in comparison with standard continuous density hidden Markov model. This indicates improved compatibility of the proposed model with the speech nature. (author)

Probability based load factors for design of concrete containment structures

International Nuclear Information System (INIS)

Hwang, H.; Kagami, S.; Reich, M.; Ellingwood, B.; Shinozuka, M.

1985-01-01

This paper describes a procedure for developing probability-based load combinations for the design of concrete containments. The proposed criteria are in a load and resistance factor design (LRFD) format. The load factors and resistance factors are derived for use in limit states design and are based on a target limit state probability. In this paper, the load factors for accident pressure and safe shutdown earthquake are derived for three target limit state probabilities. Other load factors are recommended on the basis of prior experience with probability-based design criteria for ordinary building construction. 6 refs

Constant Jacobian Matrix-Based Stochastic Galerkin Method for Probabilistic Load Flow

Directory of Open Access Journals (Sweden)

Yingyun Sun

2016-03-01

Full Text Available An intrusive spectral method of probabilistic load flow (PLF is proposed in the paper, which can handle the uncertainties arising from renewable energy integration. Generalized polynomial chaos (gPC expansions of dependent random variables are utilized to build a spectral stochastic representation of PLF model. Instead of solving the coupled PLF model with a traditional, cumbersome method, a modified stochastic Galerkin (SG method is proposed based on the P-Q decoupling properties of load flow in power system. By introducing two pre-calculated constant sparse Jacobian matrices, the computational burden of the SG method is significantly reduced. Two cases, IEEE 14-bus and IEEE 118-bus systems, are used to verify the computation speed and efficiency of the proposed method.

Modelling the flow of nitrogen and phosphorus in Europe: From loads to coastal seas

NARCIS (Netherlands)

Klepper O; Beusen AHW; Meinardi CR; CIM; LWD; LBG

1995-01-01

A model is described that aims at predicting surface water quality from N- and P-inputs on a European scale. The model combines a GIS-based approach to estimate loads, geohydrological data to define model structure and statistical techniques to estimate parameter values. The model starts with an

Un calcul de Viterbi pour un Modèle de Markov Caché Contraint

DEFF Research Database (Denmark)

Petit, Matthieu; Christiansen, Henning

2009-01-01

A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence analysis. Viterbi algorithm has been proposed to compute the most probable value....... Several constraint techniques are used to reduce the search of the most probable value of hidden states of a constrained HMM. An implementation based on PRISM, a logic programming language for statistical modeling, is presented....

Real-Time Landmine Detection with Ground-Penetrating Radar Using Discriminative and Adaptive Hidden Markov Models

Directory of Open Access Journals (Sweden)

Ho KC

2005-01-01

Full Text Available We propose a real-time software system for landmine detection using ground-penetrating radar (GPR. The system includes an efficient and adaptive preprocessing component; a hidden Markov model- (HMM- based detector; a corrective training component; and an incremental update of the background model. The preprocessing is based on frequency-domain processing and performs ground-level alignment and background removal. The HMM detector is an improvement of a previously proposed system (baseline. It includes additional pre- and postprocessing steps to improve the time efficiency and enable real-time application. The corrective training component is used to adjust the initial model parameters to minimize the number of misclassification sequences. This component could be used offline, or online through feedback to adapt an initial model to specific sites and environments. The background update component adjusts the parameters of the background model to adapt it to each lane during testing. The proposed software system is applied to data acquired from three outdoor test sites at different geographic locations, using a state-of-the-art array GPR prototype. The first collection was used as training, and the other two (contain data from more than 1200 m of simulated dirt and gravel roads for testing. Our results indicate that, on average, the corrective training can improve the performance by about 10% for each site. For individual lanes, the performance gain can reach 50%.

Letter to the Editor: Electric Vehicle Demand Model for Load Flow Studies

DEFF Research Database (Denmark)

Garcia-Valle, Rodrigo; Vlachogiannis, Ioannis (John)

2009-01-01

This paper introduces specific and simple model for electric vehicles suitable for load flow studies. The electric vehicles demand system is modelled as PQ bus with stochastic characteristics based on the concept of queuing theory. All appropriate variables of stochastic PQ buses are given...... with closed formulae as a function of charging time. Specific manufacturer model of electric vehicles is used as study case....

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

Directory of Open Access Journals (Sweden)

Yuancheng Li

2016-11-01

Full Text Available The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays. Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems.

Science.gov (United States)

Fang, Fuming; Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data.

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-08-23

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-07-26

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

Full State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon

This paper presents the design of a state estimator system for a generic helicopter based slung load system. The estimator is designed to deliver full rigid body state information for both helicopter and load and is based on the unscented Kalman filter. Two different approaches are investigated......: One based on a parameter free kinematic model and one based on a full aerodynamic helicopter and slung load model. The kinematic model approach uses acceleration and rate information from two Inertial Measurement Units, one on the helicopter and one on the load, to drive a simple kinematic model....... A simple and effective virtual sensor method is developed to maintain the constraints imposed by the wires in the system. The full model based approach uses a complex aerodynamical model to describe the helicopter together with a generic rigid body model. This rigid body model is based on a redundant...

Full State Estimation for Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon

2007-01-01

This paper presents the design of a state estimator system for a generic helicopter based slung load system. The estimator is designed to deliver full rigid body state information for both helicopter and load and is based on the unscented Kalman filter. Two different approaches are investigated......: One based on a parameter free kinematic model and one based on a full aerodynamic helicopter and slung load model. The kinematic model approach uses acceleration and rate information from two Inertial Measurement Units, one on the helicopter and one on the load, to drive a simple kinematic model....... A simple and effective virtual sensor method is developed to maintain the constraints imposed by the wires in the system. The full model based approach uses a complex aerodynamical model to describe the helicopter together with a generic rigid body model. This rigid body model is based on a redundant...

Modeling, Estimation, and Control of Helicopter Slung Load System

DEFF Research Database (Denmark)

Bisgaard, Morten

and simulating different slung load suspension types. It further includes detection and response to wire slacking and tightening, it models the aerodynamic coupling between the helicopter and the load, and can be used for multilift systems with any combination of multiple helicopters and multiple loads...

Development of Electronic Load Controllers for Free-Piston Stirling Convertors Aided by Stirling Simulation Model

Science.gov (United States)

Regan, Timothy F.

2004-01-01

The free-piston Stirling convertor end-to-end modeling effort at the NASA Glenn Research Center has produced a software-based test bed in which free-piston Stirling convertors can be simulated and evaluated. The simulation model includes all the components of the convertor: the Stirling cycle engine, heat source, linear alternator, controller, and load. So far, it has been used in evaluating the performance of electronic controller designs. Three different controller design concepts were simulated using the model: 1) Controllers with parasitic direct current loading. 2) Controllers with parasitic alternating current loading. 3) Controllers that maintain a reference current. The free-piston Stirling convertor is an electromechanical device that operates at resonance. It is the function of the electronic load controller to ensure that the electrical load seen by the machine is always great enough to keep the amplitude of the piston and alternator oscillation at the rated value. This is done by regulating the load on the output bus. The controller monitors the instantaneous voltage, regulating it by switching loads called parasitic loads onto the bus whenever the bus voltage is too high and removing them whenever the voltage is too low. In the first type of controller, the monitor-ing and switching are done on the direct-current (dc) bus. In the second type, the alternating current bus is used. The model allows designers to test a controller concept before investing time in hardware. The simulation code used to develop the model also offers detailed models of digital and analog electronic components so that the resulting designs are realistic enough to translate directly into hardware circuits.

The relationships between internal and external training load models during basketball training.

Science.gov (United States)

Scanlan, Aaron T; Wen, Neal; Tucker, Patrick S; Dalbo, Vincent J

2014-09-01

The present investigation described and compared the internal and external training loads during basketball training. Eight semiprofessional male basketball players (mean ± SD, age: 26.3 ± 6.7 years; stature: 188.1 ± 6.2 cm; body mass: 92.0 ± 13.8 kg) were monitored across a 7-week period during the preparatory phase of the annual training plan. A total of 44 total sessions were monitored. Player session ratings of perceived exertion (sRPE), heart rate, and accelerometer data were collected across each training session. Internal training load was determined using the sRPE, training impulse (TRIMP), and summated-heart-rate-zones (SHRZ) training load models. External training load was calculated using an established accelerometer algorithm. Pearson product-moment correlations with 95% confidence intervals (CIs) were used to determine the relationships between internal and external training load models. Significant moderate relationships were observed between external training load and the sRPE (r42 = 0.49, 95% CI = 0.23-0.69, p external training load and the SHRZ model (r42 = 0.61, 95% CI = 0.38-0.77, p internal and external training load models, the magnitude of the correlations and low commonality suggest that internal training load models measure different constructs of the training process than the accelerometer training load model in basketball settings. Basketball coaching and conditioning professionals should not assume a linear dose-response between accelerometer and internal training load models during training and are recommended to combine internal and external approaches when monitoring training load in players.

A Workflow to Model Microbial Loadings in Watersheds

Science.gov (United States)

Many watershed models simulate overland and instream microbial fate and transport, but few actually provide loading rates on land surfaces and point sources to the water body network. This paper describes the underlying general equations for microbial loading rates associated wit...

Development of a hysteresis model for R/C columns subjected to bi-axial lateral loading

International Nuclear Information System (INIS)

Dutta, Sekhar Chandra; Chowdhury, Rajib; Roy, Raghupati; Reddy, G. Rami

2003-01-01

Recent investigations on dynamic response of reinforced concrete (R/C) structures have confirmed that the R/C structural members undergo much more inelastic deformation in each of the two mutually perpendicular directions under bi-directional seismic loading, than that observed only under unidirectional ground motion. To predict the seismic response of R/C structure with fair accuracy demands, a faithful model that can incorporate the effect of biaxial bending interaction in column. This model should not have high computational demand but should adequately reflect the stiffness degrading and strength deterioration characteristics of R/C structural members. Present study is an effort to develop such a bi-directional hysteresis model accounting the effect of interaction between lateral loadings in two orthogonal directions. The development of the present model is based on the yield surface approach and it can incorporate both strength and stiffness degradation characteristics, which is unavoidable in R/C structures during cyclic loading. The performance of the proposed model/ is demonstrated through the prediction of available experimental results of a reinforced concrete column, subjected to biaxial loading. (author)

Optimal design of high-speed loading spindle based on ABAQUS

Science.gov (United States)

Yang, Xudong; Dong, Yu; Ge, Qingkuan; Yang, Hai

2017-12-01

The three-dimensional model of high-speed loading spindle is established by using ABAQUS’s modeling module. A finite element analysis model of high-speed loading spindle was established by using spring element to simulate bearing boundary condition. The static and dynamic performance of the spindle structure with different specifications of the rectangular spline and the different diameter neck of axle are studied in depth, and the influence of different spindle span on the static and dynamic performance of the high-speed loading spindle is studied. Finally, the optimal structure of the high-speed loading spindle is obtained. The results provide a theoretical basis for improving the overall performance of the test-bed

Demand Response Load Following of Source and Load Systems

DEFF Research Database (Denmark)

Hu, Jianqiang; Cao, Jinde; Yong, Taiyou

2017-01-01

This paper presents a demand response load following strategy for an interconnected source and load system, in which we utilize traditional units and population of cooling thermostatically controlled loads (TCLs) to follow the mismatched power caused by the load activities and the renewable power...... injection in real time. In the demand side of power systems, these TCLs are often affiliated to a bus load agent and can be aggregated to multiple TCL aggregators. Firstly, aggregate evaluation of the TCL aggregator is carried out based on a bilinear aggregate model so as to derive the available regulation...

Modelling human eye under blast loading.

Science.gov (United States)

Esposito, L; Clemente, C; Bonora, N; Rossi, T

2015-01-01

Primary blast injury (PBI) is the general term that refers to injuries resulting from the mere interaction of a blast wave with the body. Although few instances of primary ocular blast injury, without a concomitant secondary blast injury from debris, are documented, some experimental studies demonstrate its occurrence. In order to investigate PBI to the eye, a finite element model of the human eye using simple constitutive models was developed. The material parameters were calibrated by a multi-objective optimisation performed on available eye impact test data. The behaviour of the human eye and the dynamics of mechanisms occurring under PBI loading conditions were modelled. For the generation of the blast waves, different combinations of explosive (trinitrotoluene) mass charge and distance from the eye were analysed. An interpretation of the resulting pressure, based on the propagation and reflection of the waves inside the eye bulb and orbit, is proposed. The peculiar geometry of the bony orbit (similar to a frustum cone) can induce a resonance cavity effect and generate a pressure standing wave potentially hurtful for eye tissues.

Thermodynamic parameters for mixtures of quartz under shock wave loading in views of the equilibrium model

International Nuclear Information System (INIS)

Maevskii, K. K.; Kinelovskii, S. A.

2015-01-01

The numerical results of modeling of shock wave loading of mixtures with the SiO 2 component are presented. The TEC (thermodynamic equilibrium component) model is employed to describe the behavior of solid and porous multicomponent mixtures and alloys under shock wave loading. State equations of a Mie–Grüneisen type are used to describe the behavior of condensed phases, taking into account the temperature dependence of the Grüneisen coefficient, gas in pores is one of the components of the environment. The model is based on the assumption that all components of the mixture under shock-wave loading are in thermodynamic equilibrium. The calculation results are compared with the experimental data derived by various authors. The behavior of the mixture containing components with a phase transition under high dynamic loads is described

Multi-scale modeling of the thermo-hydro- mechanical behaviour of heterogeneous materials. Application to cement-based materials under severe loads

International Nuclear Information System (INIS)

Grondin, Frederic Alain

2005-01-01

The work of modeling presented here relates to the study of the thermo-hydro- mechanical behaviour of porous materials based on hydraulic binder such as concrete, High Performance Concrete or more generally cement-based materials. This work is based on the exploitation of the Digital Concrete model, of the finite element code Symphonie developed in the Scientific and Technical Centre for Building (CSTB), in coupling with the homogenization methods to obtain macroscopic behaviour laws drawn from the Micro-Macro relations. Scales of investigation, macroscopic and microscopic, has been exploited by simulation in order to allow the comprehension fine of the behaviour of cement-based materials according to thermal, hydrous and mechanical loads. It appears necessary to take into account various scales of modeling. In order to study the behaviour of the structure, we are brought to reduce the scale of investigation to study the material more particularly. The research tasks presented suggest a new approach for the identification of the multi-physic behaviour of materials by simulation. In complement of the purely experimental approach, based on observations on the sample with measurements of the apparent parameters on the macroscopic scale, this new approach allows to obtain the fine analysis of elementary mechanisms in acting within the material. These elementary mechanisms are at the origin of the evolution of the macroscopic parameters measured in experimental tests. In this work, coefficients of the thermo-hydro-mechanical behaviour law of porous materials and the equivalent hydraulic conductivity were obtained by a multi-scales approach. Applications has been carried out on the study of the damaged behaviour of cement-based materials, in the objective to determine the elasticity tensor and the permeability tensor of a High Performance Concrete at high temperatures under a mechanical load. Also, the study of the strain evolution of cement-based materials at low

Computerized Modeling and Loaded Tooth Contact Analysis of Hypoid Gears Manufactured by Face Hobbing Process

Science.gov (United States)

Nishino, Takayuki

The face hobbing process has been widely applied in automotive industry. But so far few analytical tools have been developed. This makes it difficult for us to optimize gear design. To settle this situation, this study aims at developing a computerized tool to predict the running performances such as loaded tooth contact pattern, static transmission error and so on. First, based upon kinematical analysis of a cutting machine, a mathematical description of tooth surface generation is given. Second, based upon the theory of gearing and differential geometry, conjugate tooth surfaces are studied. Then contact lines are generated. Third, load distribution along contact lines is formulated. Last, the numerical model is validated by measuring loaded transmission error and loaded tooth contact pattern.

Nonlinear modeling, strength-based design, and testing of flexible piezoelectric energy harvesters under large dynamic loads for rotorcraft applications

Science.gov (United States)

Leadenham, Stephen; Erturk, Alper

2014-04-01

There has been growing interest in enabling wireless health and usage monitoring for rotorcraft applications, such as helicopter rotor systems. Large dynamic loads and acceleration fluctuations available in these environments make the implementation of vibration-based piezoelectric energy harvesters a very promising choice. However, such extreme loads transmitted to the harvester can also be detrimental to piezoelectric laminates and overall system reliability. Particularly flexible resonant cantilever configurations tuned to match the dominant excitation frequency can be subject to very large deformations and failure of brittle piezoelectric laminates due to excessive bending stresses at the root of the harvester. Design of resonant piezoelectric energy harvesters for use in these environments require nonlinear electroelastic dynamic modeling and strength-based analysis to maximize the power output while ensuring that the harvester is still functional. This paper presents a mathematical framework to design and analyze the dynamics of nonlinear flexible piezoelectric energy harvesters under large base acceleration levels. A strength-based limit is imposed to design the piezoelectric energy harvester with a proof mass while accounting for material, geometric, and dissipative nonlinearities, with a focus on two demonstrative case studies having the same linear fundamental resonance frequency but different overhang length and proof mass values. Experiments are conducted at different excitation levels for validation of the nonlinear design approach proposed in this work. The case studies in this work reveal that harvesters exhibiting similar behavior and power generation performance at low excitation levels (e.g. less than 0.1g) can have totally different strength-imposed performance limitations under high excitations (e.g. above 1g). Nonlinear modeling and strength-based design is necessary for such excitation levels especially when using resonant cantilevers with no

Drug loading to lipid-based cationic nanoparticles

International Nuclear Information System (INIS)

Cavalcanti, Leide P.; Konovalov, Oleg; Torriani, Iris L.; Haas, Heinrich

2005-01-01

Lipid-based cationic nanoparticles are a new promising option for tumor therapy, because they display enhanced binding and uptake at the neo-angiogenic endothelial cells, which a tumor needs for its nutrition and growth. By loading suitable cytotoxic compounds to the cationic carrier, the tumor endothelial and consequently also the tumor itself can be destroyed. For the development of such novel anti-tumor agents, the control of drug loading and drug release from the carrier matrix is essential. We have studied the incorporation of the hydrophobic anti-cancer agent Paclitaxel (PXL) into a variety of lipid matrices by X-Ray reflectivity measurements. Liposome suspensions from cationic and zwitterionic lipids, comprising different molar fractions of Paclitaxel, were deposited on planar glass substrates. After drying at controlled humidity, well ordered, oriented multilayer stacks were obtained, as proven by the presence of bilayer Bragg peaks to several orders in the reflectivity curves. The presence of the drug induced a decrease of the lipid bilayer spacing, and with an excess of drug, also Bragg peaks of drug crystals could be observed. From the results, insight into the solubility of Paclitaxel in the model membranes was obtained and a structural model of the organization of the drug in the membrane was derived. Results from subsequent pressure/area-isotherm and grazing incidence diffraction (GID) measurements performed with drug/lipid Langmuir monolayers were in accordance with these conjectures

Study on Stationarity of Random Load Spectrum Based on the Special Road

Science.gov (United States)

Yan, Huawen; Zhang, Weigong; Wang, Dong

2017-09-01

In the special road quality assessment method, there is a method using a wheel force sensor, the essence of this method is collecting the load spectrum of the car to reflect the quality of road. According to the definition of stochastic process, it is easy to find that the load spectrum is a stochastic process. However, the analysis method and application range of different random processes are very different, especially in engineering practice, which will directly affect the design and development of the experiment. Therefore, determining the type of a random process has important practical significance. Based on the analysis of the digital characteristics of road load spectrum, this paper determines that the road load spectrum in this experiment belongs to a stationary stochastic process, paving the way for the follow-up modeling and feature extraction of the special road.

Neural Network Based Load Frequency Control for Restructuring ...

African Journals Online (AJOL)

Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

A model for damage load and its implications for the evolution of bacterial aging.

Directory of Open Access Journals (Sweden)

Lin Chao

2010-08-01

Full Text Available Deleterious mutations appearing in a population increase in frequency until stopped by natural selection. The ensuing equilibrium creates a stable frequency of deleterious mutations or the mutational load. Here I develop the comparable concept of a damage load, which is caused by harmful non-heritable changes to the phenotype. A damage load also ensues when the increase of damage is opposed by selection. The presence of a damage load favors the evolution of asymmetrical transmission of damage by a mother to her daughters. The asymmetry is beneficial because it increases fitness variance, but it also leads to aging or senescence. A mathematical model based on microbes reveals that a cell lineage dividing symmetrically is immortal if lifetime damage rates do not exceed a threshold. The evolution of asymmetry allows the lineage to persist above the threshold, but the lineage becomes mortal. In microbes with low genomic mutation rates, it is likely that the damage load is much greater than the mutational load. In metazoans with higher genomic mutation rates, the damage and the mutational load could be of the same magnitude. A fit of the model to experimental data shows that Escherichia coli cells experience a damage rate that is below the threshold and are immortal under the conditions examined. The model estimates the asymmetry level of E. coli to be low but sufficient for persisting at higher damage rates. The model also predicts that increasing asymmetry results in diminishing fitness returns, which may explain why the bacterium has not evolved higher asymmetry.

A cognition-based method to ease the computational load for an extended Kalman filter.

Science.gov (United States)

Li, Yanpeng; Li, Xiang; Deng, Bin; Wang, Hongqiang; Qin, Yuliang

2014-12-03

The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.

Wind Turbine Load Mitigation based on Multivariable Robust Control and Blade Root Sensors

International Nuclear Information System (INIS)

Corcuera, A Díaz de; Pujana-Arrese, A; Ezquerra, J M; Segurola, E; Landaluze, J

2014-01-01

This paper presents two H ∞ multivariable robust controllers based on blade root sensors' information for individual pitch angle control. The wind turbine of 5 MW defined in the Upwind European project is the reference non-linear model used in this research work, which has been modelled in the GH Bladed 4.0 software package. The main objective of these controllers is load mitigation in different components of wind turbines during power production in the above rated control zone. The first proposed multi-input multi-output (MIMO) individual pitch H'' controller mitigates the wind effect on the tower side-to-side acceleration and reduces the asymmetrical loads which appear in the rotor due to its misalignment. The second individual pitch H'' multivariable controller mitigates the loads on the three blades reducing the wind effect on the bending flapwise and edgewise momentums in the blades. The designed H'' controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases

Probability Based Evaluation of Vehicular Bridge Load using Weigh-in-Motion Data

Directory of Open Access Journals (Sweden)

Widi Nugraha

2016-02-01

Full Text Available Load and Resistance Factored Design (LRFD method for designing bridge in Indonesia have been implemented for more than 25 years. LRFD method treating loads and strengths variables as random variables with specific safety factors for different loads and strengths variables type. The nominal loads, load factors, reduction factors, and other criteria for bridge design code can be determined to meet the reliability criteria. Statistical data of weigh-in-motion (WIM vehicular loads measurement in Northern Java highway, Cikampek - Pamanukan, West Java (2011, used in as statistical loads variable. A 25 m simple span bridge with reinforced concrete T-girder is used as a model for structural analysis due to WIM measured and nominal vehicular load based on RSNI T-02-2005, with applied bending moment of girder as the output. The distribution fitting result of applied bending moment due to WIM measured vehicular loads is lognormal. The maximum bending moment due to RSNI T-02-2005 nominal vehicular load is 842.45 kN-m and has probability of exceedance of 5x10-5. It can be concluded, for this study, that the bridge designed using RSNI T-02-2005 is safely designed, since it has reliability index, β of 5.02, higher than target reliability, β ranging from 3.50 or 3.72.

Modeling of the mechanical behavior of austenitic stainless steels under pure fatigue and fatigue relaxation loadings

International Nuclear Information System (INIS)

Hajjaji-Rachdi, Fatima

2015-01-01

Austenitic stainless steels are potential candidates for structural components of sodium-cooled fast neutron reactors. Many of these components will be subjected to cyclic loadings including long hold times (1 month) under creep or relaxation at high temperature. These hold times are unattainable experimentally. The aim of the present study is to propose mechanical models which take into account the involved mechanisms and their interactions during such complex loadings. First, an experimental study of the pure fatigue and fatigue-relaxation behavior of 316L(N) at 500 C has been carried out with very long hold times (10 h and 50 h) compared with the ones studied in literature. Tensile tests at 600 C with different applied strain rates have been undertaken in order to study the dynamic strain ageing phenomenon. Before focusing on more complex loadings, the mean field homogenization approach has been used to predict the mechanical behavior of different FCC metals and alloys under low cycle fatigue at room temperature. Both Hill-Hutchinson and Kroener models have been used. Next, a physically-based model based on dislocation densities has been developed and its parameters measured. The model allows predictions in a qualitative agreement with experimental data for tensile loadings. Finally, this model has been enriched to take into account visco-plasticity, dislocation climb and interaction between dislocations and solute atoms, which are influent during creep-fatigue or fatigue relaxation at high temperature. The proposed model uses three adjustable parameters only and allows rather accurate prediction of the behavior of 316L(N) steel under tensile loading and relaxation. (author) [fr

Influence of dental restorations and mastication loadings on dentine fatigue behaviour: Image-based modelling approach.

Science.gov (United States)

Vukicevic, Arso M; Zelic, Ksenija; Jovicic, Gordana; Djuric, Marija; Filipovic, Nenad

2015-05-01

The aim of this study was to use Finite Element Analysis (FEA) to estimate the influence of various mastication loads and different tooth treatments (composite restoration and endodontic treatment) on dentine fatigue. The analysis of fatigue behaviour of human dentine in intact and composite restored teeth with root-canal-treatment using FEA and fatigue theory was performed. Dentine fatigue behaviour was analysed in three virtual models: intact, composite-restored and endodontically-treated tooth. Volumetric change during the polymerization of composite was modelled by thermal expansion in a heat transfer analysis. Low and high shrinkage stresses were obtained by varying the linear shrinkage of composite. Mastication forces were applied occlusally with the load of 100, 150 and 200N. Assuming one million cycles, Fatigue Failure Index (FFI) was determined using Goodman's criterion while residual fatigue lifetime assessment was performed using Paris-power law. The analysis of the Goodman diagram gave both maximal allowed crack size and maximal number of cycles for the given stress ratio. The size of cracks was measured on virtual models. For the given conditions, fatigue-failure is not likely to happen neither in the intact tooth nor in treated teeth with low shrinkage stress. In the cases of high shrinkage stress, crack length was much larger than the maximal allowed crack and failure occurred with 150 and 200N loads. The maximal allowed crack size was slightly lower in the tooth with root canal treatment which induced somewhat higher FFI than in the case of tooth with only composite restoration. Main factors that lead to dentine fatigue are levels of occlusal load and polymerization stress. However, root canal treatment has small influence on dentine fatigue. The methodology proposed in this study provides a new insight into the fatigue behaviour of teeth after dental treatments. Furthermore, it estimates maximal allowed crack size and maximal number of cycles for a

Smart Sensor-Based Motion Detection System for Hand Movement Training in Open Surgery.

Science.gov (United States)

Sun, Xinyao; Byrns, Simon; Cheng, Irene; Zheng, Bin; Basu, Anup

2017-02-01

We introduce a smart sensor-based motion detection technique for objective measurement and assessment of surgical dexterity among users at different experience levels. The goal is to allow trainees to evaluate their performance based on a reference model shared through communication technology, e.g., the Internet, without the physical presence of an evaluating surgeon. While in the current implementation we used a Leap Motion Controller to obtain motion data for analysis, our technique can be applied to motion data captured by other smart sensors, e.g., OptiTrack. To differentiate motions captured from different participants, measurement and assessment in our approach are achieved using two strategies: (1) low level descriptive statistical analysis, and (2) Hidden Markov Model (HMM) classification. Based on our surgical knot tying task experiment, we can conclude that finger motions generated from users with different surgical dexterity, e.g., expert and novice performers, display differences in path length, number of movements and task completion time. In order to validate the discriminatory ability of HMM for classifying different movement patterns, a non-surgical task was included in our analysis. Experimental results demonstrate that our approach had 100 % accuracy in discriminating between expert and novice performances. Our proposed motion analysis technique applied to open surgical procedures is a promising step towards the development of objective computer-assisted assessment and training systems.

Effect of linear and non-linear blade modelling techniques on simulated fatigue and extreme loads using Bladed

Science.gov (United States)

Beardsell, Alec; Collier, William; Han, Tao

2016-09-01

There is a trend in the wind industry towards ever larger and more flexible turbine blades. Blade tip deflections in modern blades now commonly exceed 10% of blade length. Historically, the dynamic response of wind turbine blades has been analysed using linear models of blade deflection which include the assumption of small deflections. For modern flexible blades, this assumption is becoming less valid. In order to continue to simulate dynamic turbine performance accurately, routine use of non-linear models of blade deflection may be required. This can be achieved by representing the blade as a connected series of individual flexible linear bodies - referred to in this paper as the multi-part approach. In this paper, Bladed is used to compare load predictions using single-part and multi-part blade models for several turbines. The study examines the impact on fatigue and extreme loads and blade deflection through reduced sets of load calculations based on IEC 61400-1 ed. 3. Damage equivalent load changes of up to 16% and extreme load changes of up to 29% are observed at some turbine load locations. It is found that there is no general pattern in the loading differences observed between single-part and multi-part blade models. Rather, changes in fatigue and extreme loads with a multi-part blade model depend on the characteristics of the individual turbine and blade. Key underlying causes of damage equivalent load change are identified as differences in edgewise- torsional coupling between the multi-part and single-part models, and increased edgewise rotor mode damping in the multi-part model. Similarly, a causal link is identified between torsional blade dynamics and changes in ultimate load results.

Modelling and analysis of a novel compressed air energy storage system for trigeneration based on electrical energy peak load shifting

International Nuclear Information System (INIS)

Lv, Song; He, Wei; Zhang, Aifeng; Li, Guiqiang; Luo, Bingqing; Liu, Xianghua

2017-01-01

Highlights: • A new CAES system for trigeneration based on electrical peak load shifting is proposed. • The theoretical models and the thermodynamics process are established and analyzed. • The relevant parameters influencing its performance have been discussed and optimized. • A novel energy and economic evaluation methods is proposed to evaluate the performance of the system. - Abstract: The compressed air energy storage (CAES) has made great contribution to both electricity and renewable energy. In the pursuit of reduced energy consumption and relieving power utility pressure effectively, a novel trigeneration system based on CAES for cooling, heating and electricity generation by electrical energy peak load shifting is proposed in this paper. The cooling power is generated by the direct expansion of compressed air, and the heating power is recovered in the process of compression and storage. Based on the working principle of the typical CAES, the theoretical analysis of the thermodynamic system models are established and the characteristics of the system are analyzed. A novel method used to evaluate energy and economic performance is proposed. A case study is conducted, and the economic-social and technical feasibility of the proposed system are discussed. The results show that the trigeneration system works efficiently at relatively low pressure, and the efficiency is expected to reach about 76.3% when air is compressed and released by 15 bar. The annual monetary cost saving annually is about 53.9%. Moreover, general considerations about the proposed system are also presented.

Development of an Aeroelastic Modeling Capability for Transient Nozzle Side Load Analysis

Science.gov (United States)

Wang, Ten-See; Zhao, Xiang; Zhang, Sijun; Chen, Yen-Sen

2013-01-01

Lateral nozzle forces are known to cause severe structural damage to any new rocket engine in development during test. While three-dimensional, transient, turbulent, chemically reacting computational fluid dynamics methodology has been demonstrated to capture major side load physics with rigid nozzles, hot-fire tests often show nozzle structure deformation during major side load events, leading to structural damages if structural strengthening measures were not taken. The modeling picture is incomplete without the capability to address the two-way responses between the structure and fluid. The objective of this study is to develop a coupled aeroelastic modeling capability by implementing the necessary structural dynamics component into an anchored computational fluid dynamics methodology. The computational fluid dynamics component is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, while the computational structural dynamics component is developed in the framework of modal analysis. Transient aeroelastic nozzle startup analyses of the Block I Space Shuttle Main Engine at sea level were performed. The computed results from the aeroelastic nozzle modeling are presented.

Limitations of subjective cognitive load measures in simulation-based procedural training.

Science.gov (United States)

Naismith, Laura M; Cheung, Jeffrey J H; Ringsted, Charlotte; Cavalcanti, Rodrigo B

2015-08-01

The effective implementation of cognitive load theory (CLT) to optimise the instructional design of simulation-based training requires sensitive and reliable measures of cognitive load. This mixed-methods study assessed relationships between commonly used measures of total cognitive load and the extent to which these measures reflected participants' experiences of cognitive load in simulation-based procedural skills training. Two groups of medical residents (n = 38) completed three questionnaires after participating in simulation-based procedural skills training sessions: the Paas Cognitive Load Scale; the NASA Task Load Index (TLX), and a cognitive load component (CLC) questionnaire we developed to assess total cognitive load as the sum of intrinsic load (how complex the task is), extraneous load (how the task is presented) and germane load (how the learner processes the task for learning). We calculated Pearson's correlation coefficients to assess agreement among these instruments. Group interviews explored residents' perceptions about how the simulation sessions contributed to their total cognitive load. Interviews were audio-recorded, transcribed and subjected to qualitative content analysis. Total cognitive load scores differed significantly according to the instrument used to assess them. In particular, there was poor agreement between the Paas Scale and the TLX. Quantitative and qualitative findings supported intrinsic cognitive load as synonymous with mental effort (Paas Scale), mental demand (TLX) and task difficulty and complexity (CLC questionnaire). Additional qualitative themes relating to extraneous and germane cognitive loads were not reflected in any of the questionnaires. The Paas Scale, TLX and CLC questionnaire appear to be interchangeable as measures of intrinsic cognitive load, but not of total cognitive load. A more complete understanding of the sources of extraneous and germane cognitive loads in simulation-based training contexts is

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

Science.gov (United States)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

Characterizing Class-Specific Exposure-Viral Load Suppression Response of HIV Antiretrovirals Using A Model-Based Meta-Analysis.

Science.gov (United States)

Xu, Y; Li, Y F; Zhang, D; Dockendorf, M; Tetteh, E; Rizk, M L; Grobler, J A; Lai, M-T; Gobburu, J; Ankrom, W

2016-08-01

We applied model-based meta-analysis of viral suppression as a function of drug exposure and in vitro potency for short-term monotherapy in human immunodeficiency virus type 1 (HIV-1)-infected treatment-naïve patients to set pharmacokinetic targets for development of nonnucleoside reverse transcriptase inhibitors (NNRTIs) and integrase strand transfer inhibitors (InSTIs). We developed class-specific models relating viral load kinetics from monotherapy studies to potency normalized steady-state trough plasma concentrations. These models were integrated with a literature assessment of doses which demonstrated to have long-term efficacy in combination therapy, in order to set steady-state trough concentration targets of 6.17- and 2.15-fold above potency for NNRTIs and InSTIs, respectively. Both the models developed and the pharmacokinetic targets derived can be used to guide compound selection during preclinical development and to predict the dose-response of new antiretrovirals to inform early clinical trial design. © 2016 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

An Introduction to Infinite HMMs for Single-Molecule Data Analysis.

Science.gov (United States)

Sgouralis, Ioannis; Pressé, Steve

2017-05-23

The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Detecting Seismic Events Using a Supervised Hidden Markov Model

Science.gov (United States)

Burks, L.; Forrest, R.; Ray, J.; Young, C.

2017-12-01

We explore the use of supervised hidden Markov models (HMMs) to detect seismic events in streaming seismogram data. Current methods for seismic event detection include simple triggering algorithms, such as STA/LTA and the Z-statistic, which can lead to large numbers of false positives that must be investigated by an analyst. The hypothesis of this study is that more advanced detection methods, such as HMMs, may decreases false positives while maintaining accuracy similar to current methods. We train a binary HMM classifier using 2 weeks of 3-component waveform data from the International Monitoring System (IMS) that was carefully reviewed by an expert analyst to pick all seismic events. Using an ensemble of simple and discrete features, such as the triggering of STA/LTA, the HMM predicts the time at which transition occurs from noise to signal. Compared to the STA/LTA detection algorithm, the HMM detects more true events, but the false positive rate remains unacceptably high. Future work to potentially decrease the false positive rate may include using continuous features, a Gaussian HMM, and multi-class HMMs to distinguish between types of seismic waves (e.g., P-waves and S-waves). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.SAND No: SAND2017-8154 A

Feasibility, safety and effectiveness of combining home based malaria management and seasonal malaria chemoprevention in children less than 10 years in Senegal

DEFF Research Database (Denmark)

Tine, Roger C K; Ndour, Cheikh T; Faye, Babacar

2014-01-01

Home-based management of malaria (HMM) may improve access to diagnostic testing and treatment with artemisinin combination therapy (ACT). In the Sahel region, seasonal malaria chemoprevention (SMC) is now recommended for the prevention of malaria in children. It is likely that combinations...... of antimalarial interventions can reduce the malaria burden. This study assessed the feasibility, effectiveness and safety of combining SMC and HMM delivered by community health workers (CHWs)....

DNA motif elucidation using belief propagation.

Science.gov (United States)

Wong, Ka-Chun; Chan, Tak-Ming; Peng, Chengbin; Li, Yue; Zhang, Zhaolei

2013-09-01

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k=8∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

DNA motif elucidation using belief propagation

KAUST Repository

Wong, Ka-Chun; Chan, Tak-Ming; Peng, Chengbin; Li, Yue; Zhang, Zhaolei

2013-01-01

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ?10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/?wkc/kmerHMM. 2013 The Author(s).

DNA motif elucidation using belief propagation

KAUST Repository

Wong, Ka-Chun

2013-06-29

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ?10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors\\' websites: e.g. http://www.cs.toronto.edu/?wkc/kmerHMM. 2013 The Author(s).

Research of Impact Load in Large Electrohydraulic Load Simulator

Directory of Open Access Journals (Sweden)

Yongguang Liu

2014-01-01

Full Text Available The stronger impact load will appear in the initial phase when the large electric cylinder is tested in the hardware-in-loop simulation. In this paper, the mathematical model is built based on AMESim, and then the reason of the impact load is investigated through analyzing the changing tendency of parameters in the simulation results. The inhibition methods of impact load are presented according to the structural invariability principle and applied to the actual system. The final experimental result indicates that the impact load is inhibited, which provides a good experimental condition for the electric cylinder and promotes the study of large load simulator.

Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads

Directory of Open Access Journals (Sweden)

Jae Sang Moon

2017-12-01

Full Text Available Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES. Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.

Numerical modelling of closed-cell aluminium foam under dynamic loading

Science.gov (United States)

Hazell, Paul; Kader, M. A.; Islam, M. A.; Escobedo, J. P.; Saadatfar, M.

2015-06-01

Closed-cell aluminium foams are extensively used in aerospace and automobile industries. The understanding of their behaviour under impact loading conditions is extremely important since impact problems are directly related to design of these engineering structures. This research investigates the response of a closed-cell aluminium foam (CYMAT) subjected to dynamic loading using the finite element software ABAQUS/explicit. The aim of this research is to numerically investigate the material and structural properties of closed-cell aluminium foam under impact loading conditions with interest in shock propagation and its effects on cell wall deformation. A μ-CT based 3D foam geometry is developed to simulate the local cell collapse behaviours. A number of numerical techniques are applied for modelling the crush behaviour of aluminium foam to obtain the more accurate results. The simulation results are compared with experimental data. Comparison of the results shows a good correlation between the experimental results and numerical predictions.

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

Directory of Open Access Journals (Sweden)

Zhen-Hao Zhang

2018-01-01

Full Text Available Defence against cascading failures is of great theoretical and practical significance. A novel load capacity model with a tunable proportion is proposed. We take degree and clustering coefficient into account to redistribute the loads of broken nodes. The redistribution is local, where the loads of broken nodes are allocated to their nearest neighbours. Our model has been applied on artificial networks as well as two real networks. Simulation results show that networks get more vulnerable and sensitive to intentional attacks along with the decrease of average degree. In addition, the critical threshold from collapse to intact states is affected by the tunable parameter. We can adjust the tunable parameter to get the optimal critical threshold and make the systems more robust against cascading failures.

Application of SELECT and SWAT models to simulate source load, fate, and transport of fecal bacteria in watersheds.

Science.gov (United States)

Ranatunga, T.

2017-12-01

Modeling of fate and transport of fecal bacteria in a watershed is a processed based approach that considers releases from manure, point sources, and septic systems. Overland transport with water and sediments, infiltration into soils, transport in the vadose zone and groundwater, die-off and growth processes, and in-stream transport are considered as the other major processes in bacteria simulation. This presentation will discuss a simulation of fecal indicator bacteria source loading and in-stream conditions of a non-tidal watershed (Cedar Bayou Watershed) in South Central Texas using two models; Spatially Explicit Load Enrichment Calculation Tool (SELECT) and Soil and Water Assessment Tool (SWAT). Furthermore, it will discuss a probable approach of bacteria source load reduction in order to meet the water quality standards in the streams. The selected watershed is listed as having levels of fecal indicator bacteria that posed a risk for contact recreation and wading by the Texas Commission of Environmental Quality (TCEQ). The SELECT modeling approach was used in estimating the bacteria source loading from land categories. Major bacteria sources considered were, failing septic systems, discharges from wastewater treatment facilities, excreta from livestock (Cattle, Horses, Sheep and Goat), excreta from Wildlife (Feral Hogs, and Deer), Pet waste (mainly from Dogs), and runoff from urban surfaces. The estimated source loads from SELECT model were input to the SWAT model, and simulate the bacteria transport through the land and in-stream. The calibrated SWAT model was then used to estimate the indicator bacteria in-stream concentrations for future years based on regional land use, population and household forecast (up to 2040). Based on the reductions required to meet the water quality standards in-stream, the corresponding required source load reductions were estimated.

Cyclic Loading of Growing Tissue in a Bioreactor: Mathematical Model and Asymptotic Analysis

KAUST Repository

Pohlmeyer, J. V.

2013-10-24

A simplified 2D mathematical model for tissue growth within a cyclically-loaded tissue engineering scaffold is presented and analyzed. Such cyclic loading has the potential to improve yield and functionality of tissue such as bone and cartilage when grown on a scaffold within a perfusion bioreactor. The cyclic compression affects the flow of the perfused nutrient, leading to flow properties that are inherently unsteady, though periodic, on a timescale short compared with that of tissue proliferation. A two-timescale analysis based on these well-separated timescales is exploited to derive a closed model for the tissue growth on the long timescale of proliferation. Some sample numerical results are given for the final model, and discussed. © 2013 Society for Mathematical Biology.

Hidden Markov Model for quantitative prediction of snowfall

Indian Academy of Sciences (India)

A Hidden Markov Model (HMM) has been developed for prediction of quantitative snowfall in Pir-Panjal and Great Himalayan mountain ranges of Indian Himalaya. The model predicts snowfall for two days in advance using daily recorded nine meteorological variables of past 20 winters from 1992–2012. There are six ...

Stability Enhancement Based on Virtual Impedance for DC Microgrids with Constant Power Loads

DEFF Research Database (Denmark)

Lu, Xiaonan; Sun, Kai; Guerrero, Josep M.

2015-01-01

In this paper, a converter-based DC microgrid is studied. By considering the impact of each component in DC microgrids on system stability, a multi-stage configuration is employed, which includes the source stage, interface converter stage between buses and common load stage. In order to study th....... It can be seen that by using the proposed stabilizers, the unstable poles induced by the CPLs are forced to move into the stable region. The proposed method is verified by the MATLAB/Simulink model of multi-stage DC microgrids with three distributed power generation units.......In this paper, a converter-based DC microgrid is studied. By considering the impact of each component in DC microgrids on system stability, a multi-stage configuration is employed, which includes the source stage, interface converter stage between buses and common load stage. In order to study...... the overall stability of the above DC microgrid with constant power loads (CPLs), a comprehensive small-signal model is derived by analyzing the interface converters in each stage. The instability issue induced by the CPLs is revealed by using the criteria of impedance matching. Meanwhile, virtual...

Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Science.gov (United States)

Ockenden, Mary C.; Tych, Wlodek; Beven, Keith J.; Collins, Adrian L.; Evans, Robert; Falloon, Peter D.; Forber, Kirsty J.; Hiscock, Kevin M.; Hollaway, Michael J.; Kahana, Ron; Macleod, Christopher J. A.; Villamizar, Martha L.; Wearing, Catherine; Withers, Paul J. A.; Zhou, Jian G.; Benskin, Clare McW. H.; Burke, Sean; Cooper, Richard J.; Freer, Jim E.; Haygarth, Philip M.

2017-12-01

Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15 h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10-50 km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

Energy Technology Data Exchange (ETDEWEB)

Jang, Jin-wook [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: Jinwook@kaeri.re.kr; Seong, Seung-Hwan [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: shseong@kaeri.re.kr; Lee, Un-Chul [Department of Nuclear Engineering, Seoul National University, Shinlim-Dong, Gwanak-Gu, Seoul 151-742 (Korea, Republic of)

2007-09-15

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

International Nuclear Information System (INIS)