Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

© The Authors, published by EDP Sciences, 2018. In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods

A novel overcurrent protection method based on wide area measurement in smart grid

PowerTech is the anchor conference of the IEEE Power & Energy Society in EuropeConventional overcurrent protection settings are fixed to detect faults. Power system operation mode varies while the settings of protection devices remain constant. As a result, overcurrent protection has a small protection range and a long operating time because it is incapable of adjusting its setting online. Wide Area Measurements System (WAMS) provides synchronized and real time data which can be utilized in new protection devices. This paper proposes a novel online setting scheme which utilizes online system data to calculate real-time system operation mode. Based on the real-time operation mode, real-time fault current is calculated before fault occurring. Settings of the protection devices are by this means adjusted in real time to expand the protection area and shorten the operating time. The calculation is expanded from single source model to multi-source with Π model. In addition, interval time of settings adjustment Tchange is proposed and calculated by using hyperbolic function model. Based on this method, power system real-time operation condition can be better monitored and the real-time short circuit current can be obtained to improve protection performance. © 2013 IEEE.published_or_final_versio

Run Generation Revisited: What Goes Up May or May Not Come Down

In this paper, we revisit the classic problem of run generation. Run generation is the first phase of external-memory sorting, where the objective is to scan through the data, reorder elements using a small buffer of size M , and output runs (contiguously sorted chunks of elements) that are as long as possible. We develop algorithms for minimizing the total number of runs (or equivalently, maximizing the average run length) when the runs are allowed to be sorted or reverse sorted. We study the problem in the online setting, both with and without resource augmentation, and in the offline setting. (1) We analyze alternating-up-down replacement selection (runs alternate between sorted and reverse sorted), which was studied by Knuth as far back as 1963. We show that this simple policy is asymptotically optimal. Specifically, we show that alternating-up-down replacement selection is 2-competitive and no deterministic online algorithm can perform better. (2) We give online algorithms having smaller competitive ratios with resource augmentation. Specifically, we exhibit a deterministic algorithm that, when given a buffer of size 4M , is able to match or beat any optimal algorithm having a buffer of size M . Furthermore, we present a randomized online algorithm which is 7/4-competitive when given a buffer twice that of the optimal. (3) We demonstrate that performance can also be improved with a small amount of foresight. We give an algorithm, which is 3/2-competitive, with foreknowledge of the next 3M elements of the input stream. For the extreme case where all future elements are known, we design a PTAS for computing the optimal strategy a run generation algorithm must follow. (4) Finally, we present algorithms tailored for nearly sorted inputs which are guaranteed to have optimal solutions with sufficiently long runs

Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

© 2014 Huang, Lin, Ko, Liu, Su and Lin. Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare

Tetratricopeptide repeat domain 9A is an interacting protein for tropomyosin Tm5NM-1

<p>Abstract</p> <p>Background</p> <p>Tetratricopeptide repeat domain 9A (TTC9A) protein is a recently identified protein which contains three tetratricopeptide repeats (TPRs) on its C-terminus. In our previous studies, we have shown that TTC9A was a hormonally-regulated gene in breast cancer cells. In this study, we found that TTC9A was over-expressed in breast cancer tissues compared with the adjacent controls (P < 0.00001), suggesting it might be involved in the breast cancer development process. The aim of the current study was to further elucidate the function of TTC9A.</p> <p>Methods</p> <p>Breast samples from 25 patients including the malignant breast tissues and the adjacent normal tissues were processed for Southern blot analysis. Yeast-two-hybrid assay, GST pull-down assay and co-immunoprecipitation were used to identify and verify the interaction between TTC9A and other proteins.</p> <p>Results</p> <p>Tropomyosin Tm5NM-1 was identified as one of the TTC9A partner proteins. The interaction between TTC9A and Tm5NM-1 was further confirmed by GST pull-down assay and co-immunoprecipitation in mammalian cells. TTC9A domains required for the interaction were also characterized in this study. The results suggested that the first TPR domain and the linker fragment between the first two TPR domains of TTC9A were important for the interaction with Tm5NM-1 and the second and the third TPR might play an inhibitory role.</p> <p>Conclusion</p> <p>Since the primary function of tropomyosin is to stabilize actin filament, its interaction with TTC9A may play a role in cell shape and motility. In our previous results, we have found that progesterone-induced TTC9A expression was associated with increased cell motility and cell spreading. We speculate that TTC9A acts as a chaperone protein to facilitate the function of tropomyosins in stabilizing microfilament and it may play a role in cancer cell invasion and metastasis.</p

SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge prediction plays a critical role in assisting judges and lawyers to improve the efficiency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence information and spatial information of legal texts simultaneously. Then we design a attention residual unit, which provides auxiliary information for charge prediction. In addition, our SECaps model introduces focal loss, which relieves the problem of imbalanced charges. Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4% absolutely considerable improvements under Macro F1 in Criminal-S and Criminal-L respectively. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.Comment: 13 pages, 3figures, 5 table

Connecting VIX and Stock Index ETF with VAR and Diagonal BEKK

As stock market indexes are not tradeable, the importance and trading volume of Exchange Traded Funds (ETFs) cannot be understated. ETFs track and attempt to replicate the performance of a specific index. Numerous studies have demonstrated a strong relationship between the S&P500 Composite Index and the Volatility Index (VIX), but few empirical studies have focused on the relationship between VIX and ETF returns. The purpose of the paper is to investigate whether VIX returns affect ETF returns by using vector autoregressive (VAR) models to determine whether daily VIX returns with different moving average processes affect ETF returns. The ARCH-LM test shows conditional heteroskedasticity in the estimation of ETF returns, so that the diagonal BEKK model is used to accommodate multivariate conditional heteroskedasticity in the VAR estimates of ETF returns. Daily data on ETF returns that follow different stock indexes in the USA and Europe are used in the empirical analysis, which is presented for the full data set, as well as for the three sub-periods Before, During, and After the Global Financial Crisis. The estimates show that daily VIX returns have: (1) significant negative effects on European ETF returns in the short run; (2) stronger significant effects on single market ETF returns than on European ETF returns; and (3) lower impacts on the European ETF returns than on S&P500 returns. For the European Markets, the estimates of the mean equations tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are quite similar for the whole sample period and at least two of the three sub-periods. For the US Markets, the estimates of the mean equations also tend to differ between the whole sample period and the sub-periods, but the estimates of the matrices A and B in the Diagonal BEKK model are very similar for the whole sample period and the three sub-periods

Kinetics of thermal oxidation of 6H silicon carbide in oxygen plus trichloroethylene

In this work, the behaviors of the trichloroethylene (TCE) thermal oxidation of 6H silicon carbide (SiC) are investigated. The oxide growth of 6H SiC under different TCE concentrations (ratios of TCE to O2) follows the linear-parabolic oxidation law derived for silicon oxidation by Deal and Grove, J. Appl. Phys., 36 (1965). The oxidation rate with TCE is much higher than that without TCE and strongly depends on the TCE ratio in addition to oxidation temperature and oxidation time. The increase in oxidation rate induced by TCE is between 2.7 and 67% for a TCE ratio of 0.001-0.2 and a temperature of 1000-1150°C. Generally, the oxidation rate increases quickly with the TCE ratio for a TCE ratio less than 0.05 and then gradually saturates for a ratio larger than 0.05. The activation energy EB/A of the TCE oxidation for the TCE ratio range of 0.001-0.2 is 1.04-1.05 eV, which is a little larger than the 1.02 eV of dry oxidation. A two-step model for the TCE oxidation is also proposed to explain the experimental results. The model points out that in the SiC oxidation with TCE, the products (H2O and Cl2) of the reaction between TCE and O2 can speed up the oxidation, and hence, the oxidation rate is highly sensitive to the TCE ratio. © 2005 The Electrochemical Society. All rights reserved.published_or_final_versio