Shear rheological properties of acrylic copolymers and terpolymers suitable for potentially melt processable carbon fiber precursors

In an effort to generate melt processable polyacrylonitrile (PAN) precursor fibers suitable for conversion to carbon fibers, an acrylonitrile/methyl acrylate (AN/MA) copolymer and two acrylonitrile/methyl acrylate/acryloyl benzophenone (AN/MA/ABP) terpolymers were synthesized at molar ratios of 85/15 and 85/14/1, respectively. The termonomer (ABP) was incorporated to accelerate crosslinking via UV irradiation, which serves to prevent relaxation of orientation and flow as the temperature of the fiber is raised during thermooxidative stabilization. Two molecular weights of the terpolymer and one molecular weight of the copolymer were studied to determine the effect of the termonomer, and the effect of molecular weight (MW), on the steady shear viscosity (η) and magnitude of the complex viscosity (η*). A higher rate of increase of η as a function of time was observed for the high MW terpolymer relative to that of the copolymer over the temperature range used. Using a temperature sweep and monitoring levels of η*, a minimum was observed at lower temperatures for both terpolymers. These results suggest that copolymerization with ABP significantly increased the thermally induced kinetics of crosslinking. Comparison of the η and η* data for the low and high MW terpolymers suggested that molecular weight also significantly reduced the melt stability (increased the kinetics of crosslinking). A chemorhelogical correlation was then used to quantify the effects of the termonomer and of molecular weight on the kinetics of crosslinking of the AN terpolymers. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 2856–2865, 2004     

近地表疏松介质吸收补偿的数学模拟研究

为有效提高地震资料的分辨率,定量研究地震波在疏松介质中的吸收效应及补偿条件尤为重要.根据塔里木盆地针对性施工的微测井原始资料,采用频谱比法计算表层品质因子的分布规律;应用地震波在粘弹性介质中的传播机制,符合线性吸收机制原理,采用相移法在频率-空间域对地震波在疏松介质中的传播,进行正演及吸收衰减补偿的数学模拟.结果表明:疏松介质对地震波的高频吸收衰减,只有在特定的表层地震地质条件下,才能通过后续补偿处理得以恢复,给出了完全补偿这种吸收衰减效应的条件,为沙漠地区地震资料分辨率的合理上限提供了理论模拟数据.     

Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0% from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0% baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.      

Stability of linear discrete switched systems with delays based on average dwell time method

This paper deals with the problem of exponential stability for a class of linear discrete switched systems with constant delays.The switched systems consist of stable and unstable subsystems.Based on the average dwell time method, some switching signals will be found to guarantee exponential stability of these systems.The explicit state decay estimation is also given in the form of the solutions of linear matrix inequalities(LMIs).An example relating to networked control systems(NCSs) illustrates the effect...     

An analytical framework for local feedforward networks

Interference in neural networks occurs when learning in one area of the input space causes unlearning in another area. Networks that are less susceptible to interference are referred to as spatially local networks. To obtain a better understanding of these properties, a theoretical framework, consisting of a measure of interference and a measure of network localization, is developed. These measures incorporate not only the network weights and architecture but also the learning algorithm. Using this framework to analyze sigmoidal, multilayer perceptron (MLP) networks that employ the backpropagation learning algorithm on the quadratic cost function, we address a familiar misconception that single-hidden-layer sigmoidal networks are inherently nonlocal by demonstrating that given a sufficiently large number of adjustable weights, single-hidden-layer sigmoidal MLPs exist that are arbitrarily local and retain the ability to approximate any continuous function on a compact domain.     

Evaluating the effectiveness of augmented reality displays for a manual assembly task

The focus of this research was to examine how effectively augmented reality displays, generated with a wearable computer, could be used for aiding an operator performing a manual assembly task. Fifteen subjects were asked to assemble a computer motherboard using four types of instructional media: paper manual, computer-aided, opaque augmented reality display, and see-through augmented reality display. The time of assembly and assembly errors were measured for each type of instructional media, and a questionnaire focusing on usability was administered to each subject at the end of each condition. The results of the experiment indicated that the augmented reality conditions were more effective instructional aids for the assembly task than either the paper instruction manual or the computer-aided instruction. The see-through augmented reality display resulted in the fastest assembly times, followed by the opaque augmented reality display, the computer-aided instruction, and the paper instructions respectively. In addition, subjects made fewer errors using the augmented reality conditions compared to the computer-aided and paper instructional media. However, while the two augmented reality conditions were a more effective instructional media when time for assembly was the response measure, there were still some important usability issues associated with the augmented reality technology that were not present in the non-augmented reality conditions.     

Thromboembolism complicating surgery for cervical and uterine malignancy: incidence, risk factors, and prophylaxis

Venous thromboembolism is a leading cause of death and morbidity after extended surgery for early malignancies of the cervix and uterus. Two hundred eighty-one patients who underwent such surgery were retrospectively evaluated for associated risk factors, the incidence of clinically significant thromboembolic complications, and prophylactic value of low-dose heparin and antiembolism stockings. Significant thromboemboli were encountered in 7.8% of patients postoperatively and accounted for the only 4 postoperative deaths. Forty-five percent of patients who developed thromboemboli did so after discharge from the hospital. The preoperative risk factors found to be associated with thromboembolism, in order of statistical significance, were weight in excess of 85.5 kg, advanced clinical stage of malignancy, and radiation therapy within 6 weeks of the operative procedure. Low-dose heparin therapy and the use of antiembolism stockings as preventative measures did not appear to reduce the incidence of thromboembolic complications. A prospective study will be necessary to evaluate definitely the effectiveness of various therapeutic modalities on thromboembolism in gynecologic oncology patients.     

Reading handwritten digits: a ZIP code recognition system

A neural network algorithm-based system that reads handwritten ZIP codes appearing on real US mail is described. The system uses a recognition-based segmenter, that is a hybrid of connected-components analysis (CCA), vertical cuts, and a neural network recognizer. Connected components that are single digits are handled by CCA. CCs that are combined or dissected digits are handled by the vertical-cut segmenter. The four main stages of processing are preprocessing, in which noise is removed and the digits are deslanted, CCA segmentation and recognition, vertical-cut-point estimation and segmentation, and directly lookup. The system was trained and tested on approximately 10000 images, five- and nine-digit ZIP code fields taken from real mail     

Large-scale simulation studies in image pattern recognition

Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. Simulation studies offer one way to circumvent these obstacles. We present three closely related studies of machine-printed character recognition that rely on synthetic data generated pseudo-randomly in accordance with an explicit stochastic model of document image degradations. The unusually large scale of experiments - involving several million samples that makes this methodology possible have allowed us to compute sharp estimates of the intrinsic difficulty (Bayes risk) of concrete image recognition problems, as well as the asymptotic accuracy and domain of competency of classifiers     

An intelligent control and decision approach for adaptive systems

An intelligent control and decision-making (ICD) approach that integrates expert systems technology with adaptive algorithms is presented. The controller parameters of adaptive systems can be determined using human expertise and knowledge, and they can also be adjusted based on active monitoring and identification. Decision-making, fine tuning and inexact reasoning provide the end-user and the control engineers with a natural and integrated methodology for use with intelligent control systems. Computer simulation results demonstrate the utility of the proposed technique that is an effective intelligent control and decision approach. The ICD system is implemented using a Lisp based expert system shell on an IBM PC.