Zero field magnetic resonance spectroscopy based on Nitrogen-vacancy centers

We propose a scheme to have zero field magnetic resonance spectroscopy based on a nitrogen-vacancy center and investigate the new applications in which magnetic bias field might disturb the system under investigation. Continual driving with circularly polarized microwave fields is used to selectively address one spin state. The proposed method is applied for single molecule spectroscopy, such as nuclear quadrupole resonance spectroscopy of a $^{11}$B nuclear spin and the detection of the distance of two hydrogen nuclei in a water molecule. Our work extends applications of NV centers as a nanoscale molecule spectroscopy in the zero field regime.Comment: 11 pages, 3 figure

MaxMin-L2-SVC-NCH: A New Method to Train Support Vector Classifier with the Selection of Model's Parameters

The selection of model's parameters plays an important role in the application of support vector classification (SVC). The commonly used method of selecting model's parameters is the k-fold cross validation with grid search (CV). It is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new method is proposed to train SVC with the selection of model's parameters. Firstly, training SVC with the selection of model's parameters is modeled as a minimax optimization problem (MaxMin-L2-SVC-NCH), in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal model's parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is abandoned. A gradient-based algorithm is then proposed to solve MaxMin-L2-SVC-NCH, in which L2-SVC-NCH is solved by a projected gradient algorithm (PGA) while the maximization problem is solved by a gradient ascent algorithm with dynamic learning rate. To demonstrate the advantages of the PGA in solving L2-SVC-NCH, we carry out a comparison of the PGA and the famous sequential minimal optimization (SMO) algorithm after a SMO algorithm and some KKT conditions for L2-SVC-NCH are provided. It is revealed that the SMO algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility. The comparative experiments between MaxMin-L2-SVC-NCH and the classical parameter selection models on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained and the test accuracy is not lost to the classical models. It indicates that MaxMin-L2-SVC-NCH performs better than the other models. We strongly recommend MaxMin-L2-SVC-NCH as a preferred model for SVC task

SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture

Support vector machine (SVM) and neural networks (NN) have strong complementarity. SVM focuses on the inner operation among samples while NN focuses on the operation among the features within samples. Thus, it is promising and attractive to combine SVM and NN, as it may provide a more powerful function than SVM or NN alone. However, current work on combining them lacks true integration. To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN. SVM can be viewed as a sample attention machine. It allows us to add a sample attention module to NN to implement the main function of SVM. Class prototypes are representatives of all classes, which can be viewed as alternatives to support vectors. The memory block is used for the storage and update of class prototypes. Class prototypes and memory block effectively reduce the computational cost of sample attention and make SAMN suitable for multi-classification tasks. Extensive experiments show that SAMN achieves better classification performance than single SVM or single NN with similar parameter sizes, as well as the previous best model for combining SVM and NN. The sample attention mechanism is a flexible module that can be easily deepened and incorporated into neural networks that require it

Genome-Wide Analysis of the PvHsp20 Family in Switchgrass: Motif, Genomic Organization, and Identification of Stress or Developmental-Related Hsp20s

Hsp20 proteins exist in all plant species and represent the most abundant small heat shock proteins (sHSPs) in plants. Hsp20s were known as chaperones maintaining cellular homeostasis during heat or other kinds of abiotic stresses. The objective of this study was to understand the phylogenetic relationship, genomic organization, diversification of motif modules, genome localization, expression profiles, and interaction networks of switchgrass (Panicum virgatum L.) Hsp20s (PvHsp20s). A total of 63 PvHsp20s were identified with their consensus as well as unique ACD motifs and gene structures analyzed. Most PvHsp20s (87%) were responsive to heat and other kinds of abiotic stresses. When under optimum growth condition, 38 of them displayed relative higher expression levels in inflorescence and seeds, suggesting their protective roles in the stress-sensitive reproductive organs. An in silico analysis of interaction network of PvHsp20 proteins further revealed potential interactive proteins, including stress-inducible ones in the network. Furthermore, PvHsp20 genes unevenly distributed in two sets of homeologous chromosomes, and only segmental duplication was found among the paralogous gene pairs, reflecting that the allotetraploidization of switchgrass allowed the accumulation of PvHsp20s that in turn facilitated its successful adaptation in hot and dry plateaus of North America. The present results provided an insight into PvHsp20s with an emphasis on the uniqueness of this gene family in switchgrass. Such information shall also be useful in functional studies of PvHsp20 genes and molecular breeding of switchgrass

Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction

National Natural Science Foundation of China [61271337]; Natural Science Foundations of Fujian Province of China [2011J01373]; Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, PR China [SCIP2011004]Piecewise linear representation (PLR) and back-propagation artificial neural network (BPN) have been integrated for the stock trading signal prediction recently (PLR-BPN). However, there are some disadvantages in avoiding over-fitting, trapping in local minimum and choosing the threshold of the trading decision. Since support vector machine (SVM) has a good way to avoid over-fitting and trapping in local minimum, we integrate PLR and weighted SVM (WSVM) to forecast the stock trading signals (PLR-WSVM). The new characteristics of PLR-WSVM are as follows: (1) the turning points obtained from PLR are set by different weights according to the change rate of the closing price between the current turning point and the next one, in which the weight reflects the relative importance of each turning point; (2) the prediction of stock trading signal is formulated as a weighted four-class classification problem, in which it does not need to determine the threshold of trading decision; (3) WSVM is used to model the relationship between the trading signal and the input variables, which improves the generalization performance of prediction model; (4) the history dataset is divided into some overlapping training-testing sets rather than training-validation-testing, which not only makes use of data fully but also reduces the time variability of data; and (5) some new technical indicators representing investors' sentiment are added to the input variables, which improves the prediction performance. The comparative experiments among PLR-WSVM, PLR-BPN and buy-and-hold strategy (BHS) on 20 shares from Shanghai Stock Exchange in China show that the prediction accuracy and profitability of PLR-WSVM are all the best, which indicates PLR-WSVM is effective and can be used in the stock trading signal prediction. (C) 2012 Elsevier B.V. All rights reserved

An algorithm for highway vehicle detection based on convolutional neural network

Abstract In this paper, we present an efficient and effective framework for vehicle detection and classification from traffic surveillance cameras. First, we cluster the vehicle scales and aspect ratio in the vehicle datasets. Then, we use convolution neural network (CNN) to detect a vehicle. We utilize feature fusion techniques to concatenate high-level features and low-level features and detect different sizes of vehicles on different features. In order to improve speed, we naturally adopt fully convolution architecture instead of fully connection (FC) layers. Furthermore, recent complementary advances such as batch-norm, hard example mining, and inception have been adopted. Extensive experiments on JiangSuHighway Dataset (JSHD) demonstrate the competitive performance of our method. Our framework obtains a significant improvement over the Faster R-CNN by 6.5% mean average precision (mAP). With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, three times faster than the Faster R-CNN

Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations

Fault diagnosis of transformer based on random forest

Conference Name:2011 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011. Conference Address: Shenzhen, Guangdong, China. Time:March 28, 2011 - March 29, 2011.Hunan University; Changsha University of Science and Technology; Hunan University of Science and Technology; Intelligence Computation Technology and Automation SocietyFault diagnosis of transformer in power system is studied in this paper. Considering the excellent performances of Random Forest (RF) in pattern recognition, we apply RF to construct a diagnosis model to predict the situation of transformer. The experiments of fault diagnosis for some real transformers show that RF obtains a better result in prediction accuracy and stability than traditional Back Propagation neural network does. In addition, the order of influence factors given by RF is helpful in fault diagnosis. ? 2011 IEEE