Hydrocarbon flame inhibition by C6F12O (Novec 1230): Unstretched burning velocity measurements and predictions

This study experimentally and numerically determined the unstretched laminar burning velocity of premixed CH₄-air and C₃H₈-air flames with added C₆F₁₂O (Novec 1230). High-speed shadowgraphy of spherically expanding flames provided the burning velocity as a function of stretch, which was extrapolated to zero-stretch conditions using non-linear methods. Unstretched burning velocities predicted using a recently assembled kinetic mechanism were generally in good agreement with the measurements, but tended to have poorer agreement for highly-inhibited very lean flames. To benchmark the performance of C₆F₁₂O, measured burning velocities were compared to previously measured burning velocities of premixed flames inhibited by CF₃Br (Halon 1301). The two agents provided similar reductions in the burning velocity of rich flames, whereas CF₃Br provided greater reductions when added to stoichiometric and lean flames. Lastly, experiments and simulations showed the influence of water vapor on the burning velocity of lean C₃H₈-air flames with added C₆F₁₂O. The presence of water vapor in the oxidizer increased the burning velocity by up to 23% when the fluorine to hydrogen ratio of the reactant mixture was greater than unity.     

Detecting outlier pairs in complex network based on link structure and semantic relationship

In this paper, we propose an outlier pair detection method, called LSOutPair, which discovers the vast differences between link structure and semantic relationship. LSOutPair addresses three important challenges: (1) how can we measure the target object's link similarity among multi-typed objects and multi-typed relations? (2) how can we measure the semantic similarity using the short texts? (3) how can we find the objects’ maximum differences between link structure and semantic relationship? To tackle these challenges, LSOutPair applies three main techniques: (1) two matrices are used to store link similarity and semantic similarity, (2) a k-step index algorithm, which calculates the term weighting for each object, (3) applying the linear transformation of Frobenius norm to matrices can obtain the top-K outlier pairs. LSOutPair considers link and semantics in complex network simultaneously, which is a new attempt in data mining. Substantial experiments show that LSOutPair is very effective for outlier pair detection.     

A machine-vision inspection system for conveying attitudes of columnar objects in packing processes

This paper is a new study on developing a machine vision system for inspecting the conveying attitudes of columnar objects. The presented system consists of image pre-processing, feature extraction, and attitude diagnosis. First of all, in order to segment the objects from the background (namely image pre-processing), an improved maximum between-class variance method is proposed for searching a histogram peak and calculating a threshold value based on the statistics and probability, to solve the problems caused by the non-uniform brightness in a realistic conveyor belt. Then, an open morphological operation is used to eliminate the noise from the binary images produced in the pre-processing step. In the second step (feature extraction), the features of columnar objects are determined by four methods, edge line detecting method, intercepting method, rectangle locating method and feature statistic method. Finally, the diagnosis for the conveying attitudes of columnar objects is based on a hybrid classifier using random forests, and a fuzzy logic. The proposed system is applied to a realistic process for packing industrial explosives. The results of experiments show that the proposed system allows efficient and accurate 100% inspection for the conveying attitude, which ensures the high speed and steady operation of a packing line.     

Iterated non-linear model predictive control based on tubes and contractive constraints

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint. To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle.     

A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Stock market prediction is of great interest to stock traders and investors due to high profit in trading the stocks. A successful stock buying/selling generally occurs near price trend turning point. Thus the prediction of stock market indices and its analysis are important to ascertain whether the next day's closing price would increase or decrease. This paper, therefore, presents a simple IIR filter based dynamic neural network (DNN) and an innovative optimized adaptive unscented Kalman filter for forecasting stock price indices of four different Indian stocks, namely the Bombay stock exchange (BSE), the IBM stock market, RIL stock market, and Oracle stock market. The weights of the dynamic neural information system are adjusted by four different learning strategies that include gradient calculation, unscented Kalman filter (UKF), differential evolution (DE), and a hybrid technique (DEUKF) by alternately executing the DE and UKF for a few generations. To improve the performance of both the UKF and DE algorithms, adaptation of certain parameters in both these algorithms has been presented in this paper. After predicting the stock price indices one day to one week ahead time horizon, the stock market trend has been analyzed using several important technical indicators like the moving average (MA), stochastic oscillators like K and D parameters, WMS%R (William indicator), etc. Extensive computer simulations are carried out with the four learning strategies for prediction of stock indices and the up or down trends of the indices. From the results it is observed that significant accuracy is achieved using the hybrid DEUKF algorithm in comparison to others that include only DE, UKF, and gradient descent technique in chronological order. Comparisons with some of the widely used neural networks (NNs) are also presented in the paper.     

Feature extraction and soft computing methods for aerospace structure defect classification

This study concerns the effectiveness of several techniques and methods of signals processing and data interpretation for the diagnosis of aerospace structure defects. This is done by applying different known feature extraction methods, in addition to a new CBIR-based one; and some soft computing techniques including a recent HPC parallel implementation of the U-BRAIN learning algorithm on Non Destructive Testing data. The performance of the resulting detection systems are measured in terms of Accuracy, Sensitivity, Specificity, and Precision. Their effectiveness is evaluated by the Matthews correlation, the Area Under Curve (AUC), and the F-Measure. Several experiments are performed on a standard dataset of eddy current signal samples for aircraft structures. Our experimental results evidence that the key to a successful defect classifier is the feature extraction method – namely the novel CBIR-based one outperforms all the competitors – and they illustrate the greater effectiveness of the U-BRAIN algorithm and the MLP neural network among the soft computing methods in this kind of application.     

New MPPT algorithm for PV applications based on hybrid dynamical approach

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.     

Robust techniques for abandoned and removed object detection based on Markov random field

This paper presents a novel framework for detecting abandoned objects by introducing a fully-automatic GrabCut object segmentation. GrabCut seed initialization is treated as a background (BG) modelling problem that focuses only on unhanded objects and objects that become immobile. The BG distribution is constructed with dual Gaussian mixtures that are comprised of high and low learning rate models. We propose a primitive BG model-based removed object validation and Haar feature-based cascade classifier for still-people detection once a candidate for a released object has been detected. Our system can obtain more robust and accurate results for real environments based on evaluations of realistic scenes from CAVIAR, PETS2006, CDnet 2014, and our own datasets.     

Open-circuit voltage exceeding the outermost HOMO-LUMO offset in cascade organic solar cells

We demonstrate a planar organic solar cell with a four-layer cascade architecture that exhibits an open-circuit voltage (Voc) greater than the offset in energy between the highest occupied molecular orbital (HOMO) of the outermost donor and the lowest unoccupied molecular orbital (LUMO) of the outermost acceptor. The device consists of a subphthalocyanine (SubPc)/fullerene (C60) heterojunction that is modified by inserting one or two additional donor layers between SubPc and the anode. We find that two-, three- and four-layer structures yield similar Voc (1.0 V, 0.91 V and 0.94 V, respectively), even though the outermost HOMO-LUMO offset decreases from 1.4 eV to 1.10 eV, and to 0.9 eV, respectively. Analysis of the turn-on voltage in dark provides further evidence that open-circuit voltage is not limited by the outermost HOMO-LUMO offset.