Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

Mechanical Properties and Microstructure of Bionic Non-Smooth Stainless Steel Surface by Laser Multiple Processing

Laser multiple processing, i.e. laser surface texturing and then Laser Shock Processing (LSP), is a new surface processing technology for the preparation of bionic non-smooth surfaces. Based on engineering bionics, samples of bionic non-smooth surfaces of stainless steel 0Crl 8Ni9 were manufactured in the form of reseau structure by laser multiple processing. The mechanical properties (including microhardness, residual stress, surface roughness) and microstructure of the samples treated by laser multiple processing were compared with those of the samples without LSP The results show that the mechanical properties of these samples by laser multiple processing were clearly improved in comparison with those of the samples without LSP The mechanisms underlying the improved surface microhardness and surface residual stress were analyzed, and the relations between hardness, comnressive residual stress and roughness were also presented.

Unique microstructure and property of a 2024 aluminum alloy subjected to upsetting extrusion multiple processing

The microstructure and hardness of a 2024 aluminum alloy subjected tomulti-pass upsetting extrusion at ambient temperature were studied. Experimental results indicatedthat with the number of upsetting extrusion passes increasing, the grains of the alloy are graduallyrefined and the hardness increases correspondingly. After ten passes of upsetting extrusionprocessing, the grain size decreases to less than 200 nm in diameter and the sample maintains itsoriginal shape, while the hardness is double owing to equal-axial ultrafine grains and workhardening effect caused by large plastic deformation.

CHANNEL ERROR CORRECTION FOR WIDEBAND SAR-A JOINT MULTIPLE SUBPULSES PROCESSING METHOD

A novel method,referred to as joint multiple subpulses processing,is developed to calibrate the nonideal transfer function of radio frequency front-end and I/Q imbalance in quadrature modulate/demodulate systems simultaneously,which frequently occur in wideband Synthetic Aperture Radar(SAR) systems.Based on the time-frequency relation of the chirp signal and the analyses of the channel errors in wideband SAR,joint multiple subpulses processing method is adopted to separate the image frequency component due to the I/Q channel error.Then,the complete description of the channel error is acquired for building the correction function,which is used to correct the radar raw echo in frequency domain.The validity and capability of this method are demonstrated by the experiments of the channel error correction on the high resolution SAR system with the effective bandwidth of 500 MHz.

Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an image.This technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same.When tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same object.Therefore,this paper attempts to determine the same object present in different images using color information among the unique information of the object.Thus,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television applications.The proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other images.To this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile range.The Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the object.Through the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different images.In the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 s.Through this,it is possible to detect and track the same object in real time when using the proposed method.

Multiple-response optimization for melting process of aluminum melting furnace based on response surface methodology with desirability function

To reduce the fuel consumption and emissions and also enhance the molten aluminum quality, a mathematical model with user-developed melting model and burning capacity model, were established according to the features of melting process of regenerative aluminum melting furnaces. Based on validating results by heat balance test for an aluminum melting furnace, CFD （computational fluid dynamics） technique, in association with statistical experimental design were used to optimize the melting process of the aluminum melting furnace. Four important factors influencing the melting time, such as horizontal angle between burners, height-to-radius ratio, natural gas mass flow and air preheated temperature, were identified by PLACKETT-BURMAN design. A steepest descent method was undertaken to determine the optimal regions of these factors. Response surface methodology with BOX-BEHNKEN design was adopted to further investigate the mutual interactions between these variables on RSD （relative standard deviation） of aluminum temperature, RSD of furnace temperature and melting time. Multiple-response optimization by desirability function approach was used to determine the optimum melting process parameters. The results indicate that the interaction between the height-to-radius ratio and horizontal angle between burners affects the response variables significantly. The predicted results show that the minimum RSD of aluminum temperature （12.13%）, RSD of furnace temperature （18.50%） and melting time （3.9 h） could be obtained under the optimum conditions of horizontal angle between burners as 64°, height-to-radius ratio as 0.3, natural gas mass flow as 599 m3/h, and air preheated temperature as 639 ℃. These predicted values were further verified by validation experiments. The excellent correlation between the predicted and experimental values confirms the validity and practicability of this statistical optimum strategy.

Evaluation of Productive Plant Landscapes in Cold Regions Based on a Multiple Cropping Model

Four varieties of each rapeseed and buckwheat were planted in different sowing periods to explore a variety of planting patterns.A theoretical foundation was provided for the innovative application of cold region productive plant landscapes.The analytic hierarchy process was employed to develop a model for the evaluation of multiple cropping systems.A comprehensive evaluation was conducted to study 10 indicators in plant type,flower color,flowering period,flower volume,branch coverage,plot average yield,number of grains per plant,yield per plant,thousand-grain quality and ecological adaptability in four different varieties of each rapeseed and buckwheat.The results indicated that flower color,ecological adaptability,plot average yield and flower volume were the most important indicators for the value of productive plant landscapes in cold regions.Concerning the sowing period,the optimal combination of varieties and planting times were March 31 for Qingza No.5(rapeseed)and July 18 for Xinong T1211(buckwheat).

Stochastic Model for Multiple Classes and Subclasses Simple Documents Processing

The issue of document management has been raised for a long time, especially with the appearance of office automation in the 1980s, which led to dematerialization and Electronic Document Management (EDM). In the same period, workflow management has experienced significant development, but has become more focused on the industry. However, it seems to us that document workflows have not had the same interest for the scientific community. But nowadays, the emergence and supremacy of the Internet in electronic exchanges are leading to a massive dematerialization of documents;which requires a conceptual reconsideration of the organizational framework for the processing of said documents in both public and private administrations. This problem seems open to us and deserves the interest of the scientific community. Indeed, EDM has mainly focused on the storage (referencing) and circulation of documents (traceability). It paid little attention to the overall behavior of the system in processing documents. The purpose of our researches is to model document processing systems. In the previous works, we proposed a general model and its specialization in the case of small documents (any document processed by a single person at a time during its processing life cycle), which represent 70% of documents processed by administrations, according to our study. In this contribution, we extend the model for processing small documents to the case where they are managed in a system comprising document classes organized in subclasses;which is the case for most administrations. We have thus observed that this model is a Markovian ML×K/ML×K/1 queues network. We have analyzed the constraints of this model and deduced certain characteristics and metrics. In fine, the ultimate objective of our work is to design a document workflow management system, integrating a component of global behavior prediction.

Direct Observation of Carrier Transportation Process in InGaAs/GaAs Multiple Quantum Wells Used for Solar Cells and Photodetectors

The resonant excitation is used to generate photo-excited carriers in quantum wells to observe the process of the carriers transportation by comparing the photoluminescence results between quantum wells with and without a p-n junction. It is observed directly in experiment that most of the photo-excited carriers in quantum wells with a p-n junction escape from quantum wells and form photoeurrent rather than relax to the ground state of the quantum wells. The photo absorption coei^cient of multiple quantum wells is also enhanced by a p-n junction. The results pave a novel way for solar cells and photodetectors making use of low-dimensional structure.

The Cumulative Method for Multiplication and Division

This paper provides a method of the process of computation called the cumulative method, it is based upon repeated cumulative process. The cumulative method is being adapted to the purposes of computation, particularly multiplication and division. The operations of multiplication and division are represented by algebraic formulas. An advantage of the method is that the cumulative process can be performed on decimal numbers. The present paper aims to establish a basic and useful formula valid for the two fundamental arithmetic operations of multiplication and division. The new cumulative method proved to be more flexible and made it possible to extend the multiplication and division based on repeated addition/subtraction to decimal numbers.

The Distribution of Multiple Shot Noise Process and Its Integral

In this paper, we study multiple shot noise process and its integral. We analyse these two processes systematically for their theoretical distributions, based on the piecewise deterministic Markov process theory developed by Davis [1] and the martingale methodology used by Dassios and Jang [2]. The analytic expressions of the Laplace transforms of these two processes are presented. We also obtain the multivariate probability generating function for the number of jumps, for which we use a multivariate Cox process. To derive these, we assume that the Cox processes jumps, intensity jumps and primary event jumps are independent of each other. Using the Laplace transform of the integral of multiple shot noise process, we obtain the tail of multivariate distributions of the first jump times of the Cox processes, i.e. the multivariate survival functions. Their numerical calculations and other relevant joint distributions’ numerical values are also presented.

Comparison of Sensory Processing and Semantic Differentiation in Peoples with Schizophrenia, Multiple Sclerosis and Alzheimer’s Disease

Aim: The purpose of this study was comparison of sensory processing and semantic differentiation in patients with schizophrenia, multiple sclerosis (MS) and Alzheimer’s disease. Method: Our research method was causal-comparative. Statistical population were patients with schizophrenia and patients with multiple sclerosis and Alzheimer’s disease that are kept by formal caregivers in welfare centers in Guilan or by informal caregivers in families in Rasht city of Guilan province in Iran. 45 persons with MS, Alzheimer’s disease and schizophrenia (in each group 15 persons) were randomly selected by accessible sampling method in 2014. Then, subjects were asked to response to sensory profile questionnaires to measure of sensory processing and ozgood semantic differentiation questionnaires to measure of semantic differentiation, then raw data were analyzed by multiple analyses of variance with using of spss16 software. Results: P value less than 0.05, namely p 0.05), had the maximum mean in three groups. Discussion and Conclusion: Because sensory processing and semantic differentiation aren’t in a desirable condition in patients with schizophrenia, so it is necessary that the people who are at high risk of developing the disease or the initial stages of the disease, must be acted to improve their sensory processing and semantic differentiation.

Multiple time scales of fluvial processes—theory and applications

Fluvial processes comprise water flow,sediment transport and bed evolution,which normally feature distinct time scales.The time scales of sediment transport and bed deformation relative to the flow essentially measure how fast sediment transport adapts to capacity region in line with local flow scenario and the bed deforms in comparison with the flow,which literally dictates if a capacity based and/or decoupled model is justified.This paper synthesizes the recently developed multiscale theory for sediment-laden flows over erodible bed,with bed load and suspended load transport,respectively.It is unravelled that bed load transport can adapt to capacity sufficiently rapidly even under highly unsteady flows and thus a capacity model is mostly applicable,whereas a non-capacity model is critical for suspended sediment because of the lower rate of adaptation to capacity.Physically coupled modelling is critical for fluvial processes characterized by rapid bed variation.Applications are outlined on very active bed load sediment transported by flash floods and landslide dam break floods.

Cadmium-containing Wastewater Induced by SRB:Effect of Multiple Ions and Mineralization Process

The effects of various general and heavy metal ions in acidic cadmium-containing wastewater on SBR desulfurization and cadmium removal under different concentrations were studied.CrO_(4)^(2-) and S^(2-) inhibited SRB mineralization,and Mg^(2+),Ca^(2+),Fe^(2+),and Fe^(3+) promoted SRB mineralization at low concentration(<50 mg/L).The inhibitory concentrations of Cu^(2+),Mn^(2+),Co^(2+),Zn^(2+),Pb^(2+),Hg^(2+),Cr^(3+),and Ni^(2+) were 10,30,2,20,25,5,30 mg/L,respectively.The inhibition order was Co^(2+)>Hg^(2+)>Cu^(2+)>Ni^(2+)>Zn^(2+)>Pb^(2+)>Mn^(2+)>Cr^(3+).Furthermore,the inverted microscopy and scanning electron microscopy(SEM)were used to observe the sediment crystallization process from macroscopic and microscopic viewpoints in the optimized ion environment.The experimental results show that under the mineralization of SRB,cadmium sediment crystallization is mainly divided into the rapid growth of bacteria,crystal nucleus production,block formation,and crystallization occuration.At the same time,X-ray diffraction(XRD)and energy-dispersive spectra(EDS)have also confirmed the sediment and crystallization.

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.

A Genetic Algorithm for Single Machine Scheduling with Fuzzy Processing Time and Multiple Objectives

In this paper, by considering the fuzzy nature of the data in real-life problems, single machine scheduling problems with fuzzy processing time and multiple objectives are formulated and an efficient genetic algorithm which is suitable for solving these problems is proposed. As illustrative numerical examples, twenty jobs processing on a machine is considered. The feasibility and effectiveness of the proposed method have been demonstrated in the simulation.

Cognitive impairment in multiple sclerosis: lessons from cerebrospinal fluid biomarkers

Cognitive impairment is a common clinical manifestation of multiple sclerosis,but its pathophysiology is not completely understood.White and grey matter injury together with synaptic dysfunction do play a role.The measurement of biomarkers in the cerebrospinal fluid and the study of their association with cognitive impairment may provide interesting in vivo evidence of the biological mechanisms underlying multiple sclerosis-related cognitive impairment.So far,only a few studies on this topic have been published,giving interesting results that deserve further investigation.Cerebrospinal fluid biomarkers of different pathophysiological mechanisms seem to reflect different neuropsychological patterns of cognitive deficits in multiple sclerosis.The aim of this review is to discuss the studies that have correlated cerebrospinal fluid markers of immune,glial and neuronal pathology with cognitive impairment in multiple sclerosis.Although preliminary,these findings suggest that cerebrospinal fluid biomarkers show some correlation with cognitive performance in multiple sclerosis,thus providing interesting insights into the mechanisms underlying the involvement of specific cognitive domains.

A Novel Interacting Multiple-Model Method and Its Application to Moisture Content Prediction of ASP Flooding

In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic systems.Firstly,two basic models are selected as combination components due to their proved effectiveness.One is Gaussian process(GP)model,which can provide the predictive variance of the predicted output and only has several optimizing parameters.The other is regularized extreme learning machine(RELM)model,which can improve the overfitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance.Then both of the models are updated continually using meaningful new data selected by data selection methods.Furthermore,recursive methods are employed in the two models to reduce the computational burden caused by continuous renewal.Finally,the two models are combined in IMM algorithm to realize the hybrid prediction,which can avoid the error accumulation in the single-model prediction.In order to verify the performance,the proposed method is applied to the prediction of moisture content of alkali-surfactant-polymer(ASP)flooding.The simulation results show that the proposed model can match the process very well.And IMM algorithm can outperform its components and provide a nice improvement in accuracy and robustness.