Effect of additives on formation of natural gas hydrate

The formation of natural gas hydrate (NGH) is studied in this work. Kinetics data of hydrate formation with no agitation were collected at various concentrations of the aqueous solutions with different additives such as alkylpolyglucside, sodium dodecyl benzene sulfonate and potassium oxalate monohydrate. Various kinds of additive increased the formation rates of NGH and its storage capacity and reduced the induction time of NGH formation. Moreover, the storage capacity, the induction time and the hydrate formation rate were influenced by the concentration of the aqueous solution.     

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Automatic detection of a user's interest in spoken dialog plays an important role in many applications, such as tutoring systems and customer service systems. In this study, we propose a decision-level fusion approach using acoustic and lexical information to accurately sense a user's interest at the utterance level. Our system consists of three parts: acoustic/prosodic model, lexical model, and a model that combines their decisions for the final output. We use two different regression algorithms to complement each other for the acoustic model. For lexical information, in addition to the bag-of-words model, we propose new features including a level-of-interest value for each word, length information using the number of words, estimated speaking rate, silence in the utterance, and similarity with other utterances. We also investigate the effectiveness of using more automatic speech recognition (ASR) hypotheses (n-best lists) to extract lexical features. The outputs from the acoustic and lexical models are combined at the decision level. Our experiments show that combining acoustic evidence with lexical information improves level-of-interest detection performance, even when lexical features are extracted from ASR output with high word error rate.     

Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches

In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO₂). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO₂ were not significantly different from actual field VO₂ measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO₂ estimation from HR data collected in the field.     

Comparative analysis of methods for determining the metabolic rate in order to provide a balance between man and the environment

The incorrect determination of metabolic rate can be linked to discrepancies between the model of the PMV (Predicted Mean Vote) and real thermal sensation collected in field studies. Aiming to improve the correlation of the PMV model and the real thermal sensation, this work established new values for the metabolic rate: one way being called “calculated” using Newton's Method and the other called "measured" using a metabolic analyzer. Welder's activities were evaluated, through the measurements of environmental and personal variables. New values of metabolic rate were determined for this activity. The values found for the calculated form and the measured one were, respectively, 178.63 and 145.46 W/m², different from the range provided by the table of ISO 8996 (2004) for this activity (75–125 W/m²). In order to verify which of the values of the metabolic rate was closer to the real thermal sensation of PMV, a linear regression was made between the PMV and the real thermal sensation in three ways: S × PMV_tabulated (R² = 0.1749), S × PMV_calculated (R ² = 0.7481) and S × PMV_measured (R² = 0.7854). It was found that the values measured by the instrument gave a higher coefficient of determination which was chosen for the correction of the table. The correction of the table provides a value of M_predicted, that is a value of metabolic rate that corrects the values provided by the tables of ISO 8996 (2004), by means of a correction coefficient. For the welder's activities in a metal-mechanics industry, tabulated values can be multiplied by the correction coefficient 1.4648 in order to minimize inaccuracies. The PMV_predicted, obtained through the M_predicted, when related to the actual thermal sensation, provides a coefficient of determination of 0.7511, thereby improving the model of the PMV.     

gHRV: Heart rate variability analysis made easy

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results.     

Research on the measure method of complaint theme influence on online social network

Consumer complaints on online social network quickly become online groups complaints through many people’s aggregation and looking on, interaction and word-of-mouth communication. Therefore, assessing and managing online complain influence has become a new problem for enterprise to listen to and manage online group complaints. This paper analyzed the complaint information feature of consumer group on online social network, from three-dimensional perspective of complaint text’s quality, transmission timeliness and user interaction degree. We built the influence measure model of online complaint theme based on entropy weight model by monitoring and analyzing real-time the static and dynamic properties of complaint information, explored the measure method of complaint theme influence, employed empirical method to verify the validity and provided scientific decision-making tools and methods for enterprise listening to and managing online group complains.     

基于Gabor多尺度空间的不变兴趣点检测

针对以往仿射不变兴趣点的特征尺度不能直接断定的问题, 提出一种基于Gabor多尺度空间的不变兴趣点检测算法。该算法主要包括三个步骤：应用Gabor滤波器组与图像卷积建立图像Gabor多尺度空间; 通过极大值准则检测兴趣点并直接断定特征尺度; 采用二阶矩矩阵描述兴趣点局部结构。实验结果表明, 相比较其他Hessian-Affine、MSER等算法, 该算法在图像模糊和JPEG压缩情况下可重复率和可匹配率均取得最好结果, 是一种能有效直接提取特征尺度的兴趣点检测算法。     

基于海量搜索历史数据的用户兴趣模型

针对互联网搜索引擎环境中,基于海量搜索历史数据分析用户兴趣的问题,提出一种改进的用户兴趣模型。该模型根据用户搜索的历史数据,结合向量空间模型（ VSM ）和TF-IDF算法,递归地回溯出用户兴趣权重列表。为解决用户兴趣变化和时间性能的问题,该模型引入时间遗忘机制进行动态更新,并在Hadoop分布式系统架构下利用MapReduce分布式编程模型进行实现。实验结果表明,改进的用户兴趣模型的查准率和召回率都能达到50',具有较好的可行性和可用性。