The Application of Inferential Model in Context Selection of Discourse Coherence

The selection of context is not random but limited by linguistic communicators'cognitive environment.Sperber &Wilson propose a more persuasive inferential model on the basis of Grice's theory.They think language communication is a de ductive and inferential process in which the communicators deal with the information in the context,a set of assumptions,and finally infer the meaning.Since a discourse is the work of language and the product of the process of linguistic communication,the inferential model will also adapt to the discourse analysis.With the inferential model as its basic analyzing framework,this paper puts discourse in a dynamic communicative process to investigate how the discourse receiver selects the context to make the com munication succeed.

Temperature inferential control of a reactive distillation column with double reactive sections

Temperature inferential control (TIC) is studied for a reactive distillation column with double reactive sections (RDC-DRSs) processing a hypothetical two-stage consecutive reversible reaction (A + B■C + D, C + B■E + D with αD > αB > αC > αA > αE). Because of the complicated dynamic behaviors, the controlled stages by sensitivity analysis lead to great steady-state deviations (SSDs) in top and bottom product purities. Since TIC involves considerably reduced settling times in comparison with direct composition control, small SSDs in product qualities correspond generally to small transient deviations (TDs) in product qualities. An objective function that measures SSDs in product qualities is formulated to represent the performance of a TIC system and an iterative procedure is devised to search for the best control configuration. The application of the procedure to the RDC-DRS gives considerably suppressed TDs and SSDs in top and bottom product qualities as compared with the one by sensitivity analysis. The method is simpler in principle and less computationally intensive than the current practice. These striking outcomes show the effectiveness of the proposed principle for the development of TIC systems for complicated reactive distillation columns.

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column （DWC） is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control （MPC） is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and tem- perature inferent/al control are considered. The multiobjective genetic algor/thm function ＂gamult/obj＂ in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and tempera- ture inferential control, resulting in a more stable and superior performance with lower values of integral of squared error （ISE）.

Linear Inferential Modeling: Theoretical Perspectives, Extensions, and Comparative Analysis

Inferential models are widely used in the chemical industry to infer key process variables, which are challenging or expensive to measure, from other more easily measured variables. The aim of this paper is three-fold: to present a theoretical review of some of the well known linear inferential modeling techniques, to enhance the predictive ability of the regularized canonical correlation analysis (RCCA) method, and finally to compare the performances of these techniques and highlight some of the practical issues that can affect their predictive abilities. The inferential modeling techniques considered in this study include full rank modeling techniques, such as ordinary least square (OLS) regression and ridge regression (RR), and latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS) regression, and regularized canonical correlation analysis (RCCA). The theoretical analysis shows that the loading vectors used in LVR modeling can be computed by solving eigenvalue problems. Also, for the RCCA method, we show that by optimizing the regularization parameter, an improvement in prediction accuracy can be achieved over other modeling techniques. To illustrate the performances of all inferential modeling techniques, a comparative analysis was performed through two simulated examples, one using synthetic data and the other using simulated distillation column data. All techniques are optimized and compared by computing the cross validation mean square error using unseen testing data. The results of this comparative analysis show that scaling the data helps improve the performances of all modeling techniques, and that the LVR techniques outperform the full rank ones. One reason for this advantage is that the LVR techniques improve the conditioning of the model by discarding the latent variables (or principal components) with small eigenvalues, which also reduce the effect of the noise on the model prediction. The results also show that PCR and PLS have comparable performances, and that RCCA can provide an advantage by optimizing its regularization parameter.

Inferential Statistics and Machine Learning Models for Short-TermWind Power Forecasting

The inherent randomness,intermittence and volatility of wind power generation compromise the quality of the wind power system,resulting in uncertainty in the system’s optimal scheduling.As a result,it’s critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation.Inferred statistics are utilized in this research to infer general features based on the selected information,confirming that there are differences between two forecasting categories:Forecast Category 1(0-11 h ahead)and Forecast Category 2(12-23 h ahead).In z-tests,the null hypothesis provides the corresponding quantitative findings.To verify the final performance of the prediction findings,five benchmark methodologies are used:Persistence model,LMNN(Multilayer Perceptron with LMlearningmethods),NARX(Nonlinear autoregressive exogenous neural networkmodel),LMRNN(RNNs with LM training methods)and LSTM(Long short-term memory neural network).Experiments using a real dataset show that the LSTM network has the highest forecasting accuracy when compared to other benchmark approaches including persistence model,LMNN,NARX network,and LMRNN,and the 23-steps forecasting accuracy has improved by 19.61%.

模态逻辑GL的基于广义谢弗竖的分析性模态公理系统(英文)

基于广义谢弗竖这种新算子,本文构造了模态逻辑GL的模态表列和分析性模态公理系统。广义谢弗竖是一种n元算子,为模态逻辑的表达式提供一种新记法,使分析性模态公理系统的陈述直接明了。由于谢弗竖是一种新算子,基于它的模态表列规则与通常的基于模态词和联结词的表列规则有所不同。分析性模态公理系统中的内定理证明很简单。因为分析性模态公理系统与模态表列之间存在某种对应关系,所以GL的分析性模态公理系统的完全性由GL的模态表列的完全性结果易证。GL的模态系统的完全性证明比较特殊,无法直接应用证明模态逻辑完全性的一般方法——典范模型方法,需要用一种过滤的方法挑出一些可能世界构造有穷模型。

Multivariate Statistical Process Monitoring and Control: Recent Developments and Applications to Chemical Industry

Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.

Latent Variable Regression for Supervised Modeling and Monitoring

A latent variable regression algorithm with a regularization term(r LVR) is proposed in this paper to extract latent relations between process data X and quality data Y. In rLVR,the prediction error between X and Y is minimized, which is proved to be equivalent to maximizing the projection of quality variables in the latent space. The geometric properties and model relations of rLVR are analyzed, and the geometric and theoretical relations among r LVR, partial least squares, and canonical correlation analysis are also presented. The rLVR-based monitoring framework is developed to monitor process-relevant and quality-relevant variations simultaneously. The prediction and monitoring effectiveness of rLVR algorithm is demonstrated through both numerical simulations and the Tennessee Eastman(TE) process.

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

A New Maximum Test via the Dependent Samples t-Test and the Wilcoxon Signed-Ranks Test

A maximum test in lieu of forcing a choice between the two dependent samples t-test and Wilcoxon signed-ranks test is proposed. The maximum test, which requires a new table of critical values, maintains nominal α while guaranteeing the maximum power of the two constituent tests. Critical values, obtained via Monte Carlo methods, are uniformly smaller than the Bonferroni-Dunn adjustment, giving it power superiority when testing for treatment alternatives of shift in location parameter when data are sampled from non-normal distributions.

Enhanced temperature difference control of distillation columns based on the averaged absolute variation magnitude

Temperature difference control(TDC)schemes can clearly suppress the adverse influence of pressure variations on product quality control of various distillation columns(DCs)by employing temperature differences(TDs)between the sensitive stage temperature(T_(S))and reference stage temperature(T_(R)),i.e.,T_(S)-T_(R),to infer the controlled product qualities.However,because the TDC scheme has failed to specially take the corresponding relationship between the TD employed in each control loop and the controlled product quality into account,it may suffer from relatively large steady-state errors in the controlled product qualities.To address this problem,an enhanced TDC(ETDC)scheme is proposed in the current article,in which an enhanced TD(ETD),i.e.,T_(S)-α×T_(R),is employed to replace the conventional TD for each control loop.While the locations of the sensitive and reference stages of the ETD are respectively determined according to sensitivity analysis and SVD analysis,the adjusted coefficientαis set to be the ratio between the averaged absolute variation magnitudes(AAVMs)of the T_(S)and T_(R)so that the relationship between the T_(S)and T_(R)can be appropriately coordinated.With reference to the operations of three different distillation systems,i.e.,one conventional DC distilling an ethanol(E)/butanol(B)binary mixture,one conventional DC distilling an E/propanol(P)/B ternary mixture,and one dividing-wall distillation column distilling an E/P/B ternary mixture,the performance of the ETDC scheme is assessed by compared with the conventional TDC scheme and the double TD control(DTDC)scheme.The dynamic simulation results show that the ETDC scheme is better than the conventional TDC scheme with reduced steady-state errors in the controlled product qualities and improved dynamic responses,and is comparable with the DTDC scheme despite the less temperature measurements are employed.

On Communication Models

With the development of human society,the social hub enlarges beyond one community to the extent that the world is deemed as a community as a whole.Communication,therefore,plays an increasingly important role in our daily life.As a consequence,communication model or the definition of which is not so much a definition as a guide in communication.However,some existed communication models are not as practical as it was.This paper tries to make an overall contrast among three communication models——Coded Model,Gable's Communication Model and Ostensive Inferential Model,to see how they assist people to comprehend verbal and non-verbal communication.

A Neuro-Fuzzy Based Adaptive Set-Point Heat Exchanger Control Scheme in District Heating System

The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipment such that the temperature of hot water supply is maintained at a set-point that may be a fixed value or be compensated against the external temperature. This paper presents a novel scheme that can determine the optimal set-point of hot water supply that maximizes the energy efficiency whilst providing sufficient heating capacity to the load. This scheme is based on Adaptive Neuro-Fuzzy Inferential System. The aim of this study is to improve the overall performance of district heating systems.

p < 0.05, < 0.01, < 0.001, < 0.0001, < 0.00001, < 0.000001,or < 0.0000001...

These days when I look at scientific research papers or review manuscripts,there seems to be almost a competition to have a smaller p value as a means to present more significant findings.For example,a quick Internet search using＂p〈0.0000001＂turned up many papers even reporting their p values at this level.Can and should a smaller p value play such a role？In my opinion,it cannot.

A Study on Translation Process Based on Relevance TheoryA Study on Translation Process Based on Relevance Theory

Relevance theory belongs to the field of pragmatics. Translation is a kind of communicative activity in nature. In the frame of relevance theory, translation is the process of cognition and inference. This paper focuses on the study of translation process on the basis of relevance theory in order to improve the practice of translation.

Inference by exclusion in the red-tailed black cockatoo(Calyptorhynchus banksii)

Inference by exclusion is the ability to select a given option by excluding the others.When designed appropri­ately,tests of this ability can reveal choices that cannot be explained by associative processes.Over the past de­cade,exclusion reasoning has been explored in several non-human taxonomic groups,including birds,mainly in Corvids and Parrots.To increase our understanding of the taxonomic distribution of exclusion reasoning and,therefore,its evolution,we investigated exclusion performances in red-tailed black cockatoos(Calyptorhyn­chus banksii),an Australian relative of the Goffin cockatoo(Cacatua goffini),using a food-finding task.Cock­atoos were required to find a food item hidden in 1 of the 2 experimenter’s hands.Following training sessions in which they reliably selected the closed baited hand they had just been shown open,each individual was test­ed on 4 different conditions.Critical to demonstrating exclusion reasoning was the condition in which they were shown the empty hand and then offered a choice of both closed hands.The performance of all birds was above chance on all experimental conditions but not on an olfactory and/or cuing control condition.The results sug­gest that the birds might be able to infer by exclusion,although an explanation based on rule learning cannot be excluded.This first experiment in red-tailed black cockatoo highlights the potential of this species as a model to study avian cognition and paves the pathway for future investigations.

Actuator Fault Monitoring and Fault Tolerant Control in Distillation Columns

This paper presents, from a practical viewpoint accommodation in distillation columns. Addressing faults in an investigation of real-time actuator fault detection, propagation and industrial processes, coupled with the growing demand for higher performance, improved safety and reliability necessitates implementation of less complex alternative control strategies in the events of malfunctions in actuators, sensors and or other system components. This work demonstrates frugality in the design and implementation of fault tolerant control system by integrating fault detection and diagnosis techniques with simple active restructurable feedback controllers and with backup feedback signals and switchable reference points to accommodate actuator fault in distillation columns based on a priori assessed control structures. A multivariate statistical process monitoring based fault detection and diagnosis technique through dynamic principal components analysis is integrated with one-point control or alternative control structure for prompt and effective fault detection, isolation and accommodation. The work also investigates effects of disturbances on fault propagation and detection. Specifically, the reflux and vapor boil-up control strategy used for a binary distillation column during normal operation is switched to one point control of the more valued product by utilizing the remaining healthy actuator. The proposed approach was implemented on two distillation processes： a simulated methanol-water separation column and the benchmark Shell standard heavy oil fractionation process to assess its effectiveness.

Eco’s Semiotic Narrative of The Mysterious Flame of Queen Loana

埃科是一位身兼多重身份的学者与作家,惯于在小说创作中实践自己的符号-阐释学理论。他的第五部长篇小说《洛安娜女王的神秘火焰》,是其小说创作与理论完美结合的又一典范。通过小说独特的符号叙事,埃科表达了记忆与书籍、自我与历史之间微妙的关系,以及他对这些关系与经验的理解,反映了埃科深刻的历史观与文学观。