Domain Adaptation for POS Tagging with Contrastive Monotonic Chunk-wise Attention

Neural Processing Letters - Part of Speech (POS) tagging is a sequential labelling task and one of the core applications of Natural Language Processing. It has been a challenging problem for the...     

An Adaptive Non Reference Anchor Array Framework for Audio Retrieval in Teleconferencing Environment

In this paper, an adaptive framework for audio retrieval in live teleconferencing environments with multiple participants is proposed. The framework uses a non reference anchor array (NRA) to capture the interfering speech sources, in addition to the primary array that captures the speech source of interest (SOI). A linearly constrained-minimum variance (LC-MV) beamformer is used herein such that the signal coming from the look direction is preserved while interferences coming from the non look direction are nulled. Additionally, the reverberant component of the speech acquired by this framework is removed by a novel method that uses the linear prediction (LP) residual cepstrum. This method does not require the computation of the acoustic impulse response (AIR) of the teleconferencing room and hence is computationally efficient. The NRA framework is therefore able to remove correlated noise coming from the direction of the SOI and also dereverberating the noise free signal. The performance of the proposed framework is evaluated by conducting experiments on clean speech acquisition from distant microphone arrays. Experiments on distant speech recognition are also conducted using the TIMIT and MONC databases. Experimental results obtained from the proposed framework indicate a reasonable improvement over correlation, subspace and standard minimum variance beamforming methods. The application of the framework in audio retrieval in a live teleconferencing environment with multiple participants is also discussed.     

Thermal transport properties of carbon-assisted phase change nanocomposite

Abstract

In this study, stability, density, rheology, and thermal conductivity of the Multi-wall carbon nanotubes (MWCNTs) based PCM nanocomposites were experimentally investigated. The PCM OM08 has been selected for the air-conditioning application with a phase change temperature of 8–10?°C. PCM nanocomposites (without surfactant) in various concentrations namely 0.05, 0.1, 0.3 and 0.5?vol. % were prepared using a typical two-step method and high homogenous stability is achieved using a mechanical mixing technique (ultra-sonication). The density of PCM nanocomposites was measured using two different types of standard volumetric flask approach and the experimental values confirmed good conformity with the Pak and Cho mixing theory. The highest thermal conductivity augmentation of 24.03%and 64.04% were achieved in liquid and solid states, respectively, with 0.5?vol. % of MWCNT. While, the addition of MWCNT resulted in a changeover of Newtonian to Non-Newtonian behaviors at a low shear rate, and the dynamic viscosity enhancement was increased by 130% with 0.5?vol. % of MWCNT. Further, the results were compared with existing correlations and it was found that the experimental values were in line with existing correlations.     

Multi-electrode microbial fuel cell (MEMFC): A close analysis towards large scale system architecture

Microbial fuel cells are capable of producing electricity through the treatment of wastewater, however, the low power density poses main hurdles towards their wide application. In present work, microbial fuel cell based on multiple anodes, acting as baffle is constructed for achieving higher performance which can be scaled up for real life application. It is investigated for continuous sixty two days using distillery wastewater (WW) in three batches under ambient condition. During first batch, the WW is maintained under stagnant condition inside the anode chamber where as in the rest of the two batches WW is recirculated in the chamber. A maximum power density 427 mW m⁻², is achieved under stagnant condition which is further enhanced to 597 mW m⁻² under recirculation mode. Recirculation of WW reduces the internal resistance arising from the mass transfer by 50%. Maximum COD removal and Coulombic efficiency obtained is 43% and 23%. Biofouling on the surface of the membrane facing anode chamber is observed.     

Alginic acid‐g‐poly(N‐vinylformamide) graft copolymer: Synthesis,characerization, swelling,and flocculation property

The graft copolymer of N‐vinylformamide with alginic acid was synthesized by free radical polymerization using potassium peroxymonosulphate and thiourea as redox pair in inert atmosphere. The optimum conditions for maximum grafting have been determined by varying the concentrations of N‐vinylformamide, potassium peroxymonosulphate, thiourea, sulfuric acid, alginic acid as well as time duration and temperature. The grafting parameters increase up to the certain concentrations of N‐vinylformamide, potassium peroxymonosulhate, thiourea, and hydrogen ion while thereafter grafting parameters decrease. The effect of alginic acid concentration on grafting parameters has been observed to decrease continuously. It has also been found that grafting parameters increase up to certain time and temperature, respectively, and thereafter decrease. The swelling properties of graft copolymer in terms of swelling ratio and percent swelling are investigated. Flocculation property of pure and grafted sample for both coking and noncoking coals is also investigated for the treatment of coal mine waste water. The graft copolymer has been characterized by Fourier transform infrared spectroscopy as well as thermogravimetic analysis. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Image smog restoration using oblique gradient profile prior and energy minimization

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.     

Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming

Epilepsy, sometimes called seizure disorder, is a neurological condition that justifies itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of electrical activity in the brain that causes an alteration in behaviour, sensation, or consciousness. It is essential to have a method for automatic detection of seizures, as these seizures are arbitrary and unpredictable. A profound study of the electroencephalogram (EEG) recordings is required for the accurate detection of these epileptic seizures. In this study, an Innovative Genetic Programming framework is proposed for classification of EEG signals into seizure and nonseizure. An empirical mode decomposition technique is used for the feature extraction followed by genetic programming for the classification. Moreover, a method for intron deletion, hybrid crossover, and mutation operation is proposed, which are responsible for the increase in classification accuracy and a decrease in time complexity. This suggests that the Innovative Genetic Programming classifier has a potential for accurately predicting the seizures in an EEG signal and hints on the possibility of building a real‐time seizure detection system.     

Optimization of grooved klystron collector design for efficient heat transfer

Klystron microwave amplifiers play a vital role in addressing the increasing demands of high‐average microwave power for strategic applications such as linear accelerators, active denial technologies, radar, and so forth. Typically, klystrons have an efficiency of 50%‐60% that demands an efficient thermal design for dissipating the unused DC power in the form of spent electron beam in collector. Hence, thermal modeling of the collector for efficient heat dissipation is highly critical in design of high average power klystrons. Of several types of design, grooved collector design is widely employed so as to increase the surface area between the collector and coolant and thereby enhance heat transfer. In this article, a mathematical model and design strategy have been demonstrated to obtain the optimum dimensions, that is, height, depth, and width of fins based on film coefficient and Reynolds number. For validation, the dimensions are then simulated in a computational fluid dynamics software (ANSYS‐Fluent) demonstrating excellent agreement with the mathematical modeling. In addition, the optimum choice of grooving method (longitudinal or crossed) for the given power level has also been provided. The demonstrated strategy can also potentially be employed to other devices, which uses groove based design with water as coolant medium such as gyrotrons, plasma devices, and so forth.     

Effect of bed characteristics on local liquid spreading in a trickle bed

Trickle bed reactors are important to several chemical process applications. While the available computational fluid dynamics models can predict overall liquid volume fraction, the prediction of spatial liquid distribution continues to be a challenging task. In the present work, Eulerian multifluid simulations were performed to investigate the effects of particle size, gas and liquid flow rates, and bed structure on local liquid spreading, and the predictions were validated using measured liquid spreading. It was found that the capillary pressure force caused liquid to spread in the lateral direction and that the interphase interaction forces pushed it in the downward direction and the relative magnitudes of these forces governed the local liquid distribution. While the use of existing capillary pressure force model led to satisfactory prediction of the observed trends of dynamic and steady state local liquid spreading, the modified capillary pressure force led to quantitatively correct predictions of local liquid spreading. © 2016 American Institute of Chemical Engineers AIChE J, 63: 347–357, 2017     

高速运放支持红外接近检测设计

IR接近检测传感器广泛用于检测物体是否存在,与参照物之间的距离,或同时检测两者.具体应用包括:测速、自控式水龙头,自动计数器或传送带物体检测,打印机纸张边缘检测等其它应用.