The real-time dynamic liquid level calculation method of the sucker rod well based on multi-view features fusion

In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.

Comparison of different severity scores in correlating hemoglobin levels with the severity of hepatic decompensation: An observational study

BACKGROUND Chronic liver disease is a growing global health problem,leading to hepatic decompensation characterized by an array of clinical and biochemical complic-ations.Several scoring systems have been introduced in assessing the severity of hepatic decompensation with the most frequent ones are Child-Pugh score,model of end-stage liver disease(MELD)score,and MELD-Na score.Anemia is frequently observed in cirrhotic patients and is linked to worsened clinical outcomes.Although studies have explored anemia in liver disease,few have investigated the correlation of hemoglobin level with the severity of hepatic decompensation.AIM To determine the relationship between hemoglobin levels and the severity of decompensated liver disease and comparing the strength of this correlation using the Child-Pugh,MELD,and MELD-Na scores.METHODS This cross-sectional study was conducted at a tertiary care hospital with 652 decompensated liver disease patients enrolled in the study.Data was collected on demographics,clinical history,and laboratory findings,including hemoglobin levels,bilirubin,albumin,prothrombin time(international normalized ratio),sodium,and creatinine.The Child-Pugh,MELD,and MELD-Na scores were calculated.Statistical analysis was performed using Statistical Package for the Social Sciences version 26,and correlations between hemoglobin levels and severity scores were assessed using Spearman's correlation coefficient.RESULTS The study included 405 males(62.1%)and 247 females(37.9%)with an average age of 58.8 years.Significant inverse correlations were found between hemoglobin levels and Child-Pugh,MELD,and MELD-Na scores(P<0.01),with the MELD scoring system being the strongest correlator among all.One-way analysis of variance revealed significant differences in hemoglobin levels across the severity groups of each scoring system(P=0.001).Tukey's post hoc analysis confirmed significant internal differences among each severity group.CONCLUSION Understanding the correlation between hemoglobin and liver disease severity can improve patient management by offering insights into prognosis and guiding treatment decisions.

MULTI-PHASE ACTIVE CONTOUR MODEL FOR IMAGE SEGMENTATION BASED ON LEVEL SETS

A new multi-phase active contour model is proposed for the image segmentation. It is a generalization of the C-V model with the following characteristics： （1） A key technique, called the technique of painting background （TPBG）, is developed to remove the information of the background, which blocks the detection of weak boundaries in the object; （2） The two-phase level set is applied multiple times for getting the multi-phase segmentation model （n-1 times for the n-phase model, n〉1）; （3） A scaling-based method is introduced to improve the basic model. Experimental results show that the proposed model is effective for detecting weak boundaries.

An Operational Test Method for VSC-HVDC Valves Based on Modular Multi-level Converters

为给基于模块化多电平换流器（modular multi-levelconverter,MMC）的电压源换流器高压直流输电（voltagesourced converter HVDC,VSC-HVDC）换流阀提供有效的检验手段,以验证其设计的正确性及其运行可靠性,对换流阀的运行特性和关键应力进行分析,提出基于原理等效的MMC阀运行试验方法,设计试验电路并阐明其运行机理。搭建的PSCAD电磁暂态仿真模型和实际的试验验证证明了试验方法的正确性和有效性。

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

A Mandatory Access Control Framework in Virtual Machine System with Respect to Multi-level Security Ⅱ:Implementation

This paper is a continuation of our last paper [1] which describes the theory of Virt-BLP model. Based on Virt-BLP model,this paper implements a mandatory access control(MAC) framework applicable to multi-level security(MLS) in Xen. The Virt-BLP model is the theoretical basis of this MAC framework,and this MAC framework is the implementation of Virt-BLP model. Our last paper focuses on Virt-BLP model,while this paper concentrates on the design and implementation of MAC framework. For there is no MAC framework applicable to MLS in virtual machine system at present,our MAC framework fills the blank by applying Virt-BLP model to Xen,which is better than current researches to guarantee the security of communication between virtual machines(VMs) . The experimental results show that our MAC framework is effective to manage the communication between VMs.

A LEVEL SET METHOD FOR STRUCTURAL TOPOLOGY OPTIMIZATION WITH MULTI-CONSTRAINTS AND MULTI-MATERIALS

Combining the vector level set model,the shape sensitivity analysis theory with the gradient projection technique,a level set method for topology optimization with multi-constraints and multi-materials is presented in this paper.The method implicitly describes structural material in- terfaces by the vector level set and achieves the optimal shape and topology through the continuous evolution of the material interfaces in the structure.In order to increase computational efficiency for a fast convergence,an appropriate nonlinear speed mapping is established in the tangential space of the active constraints.Meanwhile,in order to overcome the numerical instability of general topology opti- mization problems,the regularization with the mean curvature flow is utilized to maintain the interface smoothness during the optimization process.The numerical examples demonstrate that the approach possesses a good flexibility in handling topological changes and gives an interface representation in a high fidelity,compared with other methods based on explicit boundary variations in the literature.

Design of Irregular QC-LDPC Code Based Multi-Level Coded Modulation Scheme for High Speed Optical Communication Systems

In this paper, we focus on the design of irregular QC-LDPC code based multi-level coded modulation(MLCM) scheme by jointly optimizing the component code rate and the degree distribution of the irregular QC-LDPC component code. Firstly, the sub-channel capacities of MLCM systems is analyzed and discussed, based on which the optimal component code rate can be obtained. Secondly, an extrinsic information transfer chart based two-stage searching algorithm is proposed to find the good irregular QC-LDPC code ensembles with optimal component code rates for their corresponding sub-channels. Finally, by constructing the irregular QC-LDPC component codes from the designed ensembles with the aim of possibly enlarging the girth and reducing the number of the shortest cycles, the designed irregular QC-LDPC code based 16QAM and 64QAM MLCM systems can achieve 0.4 dB and 1.2 dB net coding gain, respectively, compared with the recently proposed regular QC-LDPC code based 16QAM and 64QAM MLCM systems.

Fatigue Properties of RPC under Cyclic Loads of Single-stage and Multi-level Amplitude

Fatigue properties of Reactive Powder Concrete （RPC） under axial compression of single-stage and multi-level amplitude in cycles were studied. The tests reveal the fatigue life, the strain and residual life of the RPC samples. Through the analysis of the test results under cyclic loads of single amplitude, the S-N curve of RPC and the evolution rule of macro-damage of RPC were presented, which can be divided into latency stage, stable development stage and instability development stage according to the evolution pattern of the fatigue crack. Accordingly, the development of longitudinal deformation presents the similar three-stage-model, and the proportion of each stage is 15%, 75%, and 10%. According to test results, the fatigue strength reduction factor is 0.564. We brought forward an empirical formula to predict the life of RPC via total longitudinal strain and got the evolving rule for the residual strength of the RPC. The analysis of the test results under cyclic loads of multi-level amplitude shows that the strain under this loading pattern experiences three stages. The characteristic value for the residual strain was obtained. The irreversible damage and non-linear evolution of RPC was described by the development of the residual plastic strain.

An effective multi-level algorithm based on ant colony optimization for graph bipartitioning

Partitioning is a fundamental problem with applications to many areas including data mining, parellel processing and Very-large-scale integration （VLSI） design. An effective multi-level algorithm for bisecting graph is proposed. During its coarsening phase, an improved matching approach based on the global information of the graph core is developed with its guidance function. During the refinement phase, the vertex gain is exploited as ant＇s heuristic information and a positive feedback method based on pheromone trails is used to find the global approximate bipartitioning. It is implemented with American National Standards Institute （ANSI） C and compared to MeTiS. The experimental evaluation shows that it performs well and produces encouraging solutions on 18 different graphs benchmarks.

A new tracking error detection method using amplitude difference detection for signal waveform modulation multi-level discs

The sub-land/sub-pit affects the characteristic of the tracking error signal which is generated by the conventional differential phase detection （DPD） method in the signal waveform modulation multi-level （SWML） read-only disc. To solve this problem, this paper proposes a new tracking error detection method using amplitude difference. Based on the diffraction theory, the amplitude difference is proportional to the tracking error and is feasible to be used for obtaining the off-track information. The experimental system of the amplitude difference detection method is developed. The experimental results show that the tracking error signal derived from the new method has better performance in uniformity and signal-to-noise ratio than that derived from the conventional DPD method in the SWML read-only disc.

Integrated importance measure for multi-state coherent systems of k level

To verify the effectiveness of the integrated importance measure （IIM） for multi-state coherent systems of k level, the definition and physical meaning of IIM are demonstrated. Then, the improvement potential and Δ-importance measures are generalized to multi-state coherent systems based on the system performance level, and the relationships between IIM and traditional importance measures are discussed. The characteristics of IIM are demonstrated in both series and parallel systems. Also, an application to an oil transportation system is given. The comparison results show that： （i） IIM has some useful properties that are not possessed by traditional importance measures; （ii） IIM is effective in evaluating the component role in multi-state systems when the component reliability and the failure rate are simultaneously considered.

Dietary energy sources and levels shift the multi-kingdom microbiota and functions in the rumen of lactating dairy cows

Background: Dietary energy source and level in lactation diets can profoundly affect milk yield and composition.Such dietary effects on lactation performance are underpinned by alteration of the rumen microbiota, of which bacteria, archaea, fungi, and protozoa may vary differently. However, few studies have examined all the four groups of rumen microbes. This study investigated the effect of both the level and source of dietary energy on rumen bacteria, archaea, fungi, and protozoa in the rumen of lactating dairy cows. A 2 × 2 factorial design resulted in four dietary treatments: low and high dietary energy levels(LE: 1.52–1.53;and HE: 1.71–1.72 Mcal/kg dry matter) and two dietary energy sources(GC: finely ground corn;and SFC: steam-flaked corn). We used a replicated 4 × 4 Latin square design using eight primiparous Chinese Holstein cows with each period lasting for 21 d. The rumen microbiota was analyzed using metataxonomics based on kingdom-specific phylogenetic markers [16 S r RNA gene for bacteria and archaea, 18 S r RNA gene for protozoa, and internally transcribed spacer 1(ITS1) for fungi] followed with subsequent functional prediction using PICRUSt2.Results: The GC resulted in a higher prokaryotic(bacterial and archaeal) species richness and Faith's phylogenetic diversity than SFC. For the eukaryotic(fungi and protozoa) microbiota, the LE diets led to significantly higher values of the above measurements than the HE diets. Among the major classified taxa, 23 genera across all the kingdoms differed in relative abundance between the two dietary energy levels, while only six genera(none being protozoal)were differentially abundant between the two energy sources. Based on prokaryotic amplicon sequence variants(ASVs) from all the samples, overall functional profiles predicted using PICRUSt2 differed significantly between LE and HE but not between the two energy sources. Fish Taco analysis identified Ruminococcus and Coprococcus as the taxa potentially contributing to the enriched KEGG pathways for biosynthesis of amino acids and to the metabolisms of pyruvate, glycerophospholipid, and nicotinate and nicotinamide in the rumen of HE-fed cows. The co-occurrence networks were also affected by the dietary treatments, especially the LE and GC diets, resulting in distinct co-occurrence networks. Several microbial genera appeared to be strongly correlated with one or more lactation traits.Conclusions: Dietary energy level affected the overall rumen multi-kingdom microbiota while little difference was noted between ground corn and steam-flaked corn. Some genera were also affected differently by the four dietary treatments, including genera that had been shown to be correlated with lactation performance or feed efficiency.The co-occurrence patterns among the genera exclusively found for each dietary treatment may suggest possible metabolic interactions specifically affected by the dietary treatment. Some of the major taxa were positively correlated to milk properties and may potentially serve as biomarkers of one or more lactation traits.

Structural analysis of multi-level detachments and identification of deep-seated anticline

Based on forward modeling of detachment fold, this study presents a method to analyze multi-level detachment structures and identify the authenticity of deep-seated anticlines using time-domain seismic section. The steps include the conversion of the time-migrated seismic image into depth domain image using a constant velocity field, structural interpretation of the depth seismic image, measurement of each structural relief area and each height above reference level, plotting of area-height relationship chart with piecewise fitting etc. The area-depth correlation can help the division of structural sequences, the definition of detachment levels, the calculation of the tectonic shortening, and the identification of deep-seated structure. The segment area-height relationship is a feature of multi-level detachment structures, while little or no linear correlation between area and height is an indicator of non-deformation or pseudo-anticline. Regardless of the uncertainty of area-height relationship, the segment slopes will correspond to the differential shortenings of multi-level detachments, the intersection between adjacent segments will give the height of detachment surface above reference level and then help define the detachment level in original time-domain seismic section. This method can make use of time-domain seismic data to determine the geologic structure of complicated structure areas and assess risks of deep exploration targets. It has achieved good results in southern Junggar and eastern Sichuan areas.

MULTI-REGION SEGMENTATION OF SAR IMAGE BY A MULTIPHASE LEVEL SET APPROACH

In this letter,a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar(SAR)image is presented.Anenergy functional that is applicable for SAR image segmentation is defined.It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentationis implemented by the numeric solution of the partial differential equations.The performance of theapproach is verified by both simulation and real SAR images.The experiments show that the proposedalgorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions.

Top-level modeling theory of multi-discipline virtual prototype

Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufactur- ing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heteroge- neous models has brought great challenges to the modeling and simulation （M＆S） science and technology. This paper presents a top-level modeling theory based on the meta modeling framework （M2F） of the COllaborative SIMulation （COSlM） theory of virtual prototyping to solve the problems. Firstly the fundamental prin- ciples of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special require- ments in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the top- level modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demon- strate the feasibility of the theory.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Fabrication of multi-level 3-dimension microstructures by phase inversion process

One process based on phase inversion of fillers in microstructures for the fabrication of multi-level three-dimensional(3-D) microstructures is described using SU-8, a kind of epoxy photoresist, as the model constructing materials. This process is depicted by use of the routine photolithography technique to construct the top layer of 3-D microstructures on the bottom layer of 3-D microstructures layer by layer. This process makes it possible to fabricate multi-level 3-D microstructures with connectors at desired locations, and to seal long span microstructures(e.g. very shallow channels with depth less than 50 μm and width more than 300 μm) without blockage. In addition, this process can provide a sealing layer by the solidification of a liquid polymer layer, which can be as strong as the bulk constructing materials for microstructures due to a complete contact and cross-linking between the sealing layer and the patterned layers. The hydrodynamic testing indicates that this kind of sealing and interconnection can endure a static pressure of more than 10 MPa overnight and a hydrodynamic pressure drop of about 5.3 MPa for more than 8 hours by pumping the tetrahydrofuran solution through a 60 μm wide micro-channels.

A general multi-objective programming model for minimum ecological flow or water level of inland water bodies

Assessment of ecological flow or water level for water bodies is important for the protection of de- graded or degrading ecosystems caused by water shortage in arid regions, and it has become a key issue in water resources planning. In the past several decades, many methods have been proposed to assess ecological flow for rivers and ecological water level for lakes or wetlands. To balance water uses by human and ecosystems, we proposed a general multi-objective programming model to determine minimum ecological flow or water level for inland water bodies, where two objectives are water index for human and habitat index for ecosystems, respectively Using the weighted sum method for multi-objective optimization, minimum ecological flow or water level can be determined from the breakpoint in the water index-habitat index curve, which is similar to the slope method to de- termine minimum ecological flow from wetted perimeter-discharge curve. However, the general multi-objective programming model is superior to the slope method in its physical meaning and calculation method. This model provides a general analysis method for ecological water uses of different inland water bodies, and can be used to define minimum ecological flow or water level by choosing appropriate water and habitat indices. Several com- monly used flow or water level assessment methods were found to be special cases of the general model, including the wetted perimeter method and the multi-objective physical habitat simulation method for ecological river flow, the inundated forest width method for regeneration flow of floodplain forest and the lake surface area method for eco- logical lake level. These methods were applied to determine minimum ecological flow or water level for two repre- sentative rivers and a lake in northern Xinjiang of China, including minimum ecological flow for the Ertix River, minimum regeneration flow for floodplain forest along the midstream of Kaxgar River, and minimum ecological lake level for the Ebinur Lake. The results illustrated the versatility of the general model, and can provide references for water resources planning and ecosystem protection for these rivers and lake.

Method of multi-level recursive and application to nonlinear dynamic deformation forecasting

The time-dependence bilinear mixed-regression deformation model and time-dependence bilinear dynamic system deformation model are established for deformation observation series. According to the multi- level recursive method, the time-dependence parameters are first traced and predicted, and then the dynamic system states. Due to the method considering time-dependence of deformation and having stronger adaptability to time-dependence system, it can improve forecast’s precision. It is very effective for data processing of nonlinear dynamic deformation monitoring to make multi-step forecasting.