Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

Model-Free Feature Screening via Maximal Information Coefficient (MIC) for Ultrahigh-Dimensional Multiclass Classification

It is common for datasets to contain both categorical and continuous variables. However, many feature screening methods designed for high-dimensional classification assume that the variables are continuous. This limits the applicability of existing methods in handling this complex scenario. To address this issue, we propose a model-free feature screening approach for ultra-high-dimensional multi-classification that can handle both categorical and continuous variables. Our proposed feature screening method utilizes the Maximal Information Coefficient to assess the predictive power of the variables. By satisfying certain regularity conditions, we have proven that our screening procedure possesses the sure screening property and ranking consistency properties. To validate the effectiveness of our approach, we conduct simulation studies and provide real data analysis examples to demonstrate its performance in finite samples. In summary, our proposed method offers a solution for effectively screening features in ultra-high-dimensional datasets with a mixture of categorical and continuous covariates.

Model-Free Ultra-High-Dimensional Feature Screening for Multi-Classified Response Data Based on Weighted Jensen-Shannon Divergence

In ultra-high-dimensional data, it is common for the response variable to be multi-classified. Therefore, this paper proposes a model-free screening method for variables whose response variable is multi-classified from the point of view of introducing Jensen-Shannon divergence to measure the importance of covariates. The idea of the method is to calculate the Jensen-Shannon divergence between the conditional probability distribution of the covariates on a given response variable and the unconditional probability distribution of the covariates, and then use the probabilities of the response variables as weights to calculate the weighted Jensen-Shannon divergence, where a larger weighted Jensen-Shannon divergence means that the covariates are more important. Additionally, we also investigated an adapted version of the method, which is to measure the relationship between the covariates and the response variable using the weighted Jensen-Shannon divergence adjusted by the logarithmic factor of the number of categories when the number of categories in each covariate varies. Then, through both theoretical and simulation experiments, it was demonstrated that the proposed methods have sure screening and ranking consistency properties. Finally, the results from simulation and real-dataset experiments show that in feature screening, the proposed methods investigated are robust in performance and faster in computational speed compared with an existing method.

Three-Dimensional TIN Algorithm for Digital Terrain Modeling

The problem of taking an unorganized point cloud in 3D space and fitting a polyhedral surface to those points is both important and difficult. Aiming at increasing applications of full three dimensional digital terrain surface modeling, a new algorithm for the automatic generation of three dimensional triangulated irregular network from a point cloud is pro- posed. Based on the local topological consistency test, a combined algorithm of constrained 3D Delaunay triangulation and region-growing is extended to ensure topologically correct reconstruction. This paper also introduced an efficient neighbor- ing triangle location method by making full use of the surface normal information. Experimental results prove that this algo- rithm can efficiently obtain the most reasonable reconstructed mesh surface with arbitrary topology, wherein the automati- cally reconstructed surface has only small topological difference from the true surface. This algorithm has potential applica- tions to virtual environments, computer vision, and so on.

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Research on dynamic updating of three dimensional geological modeling based on the OO-Solid model

The dynamic updating of the model included: the change of space border,addi- tion and reduction of spatial component (disappearing,dividing and merging),the change of the topological relationship and synchronous dynamic updating of database.Firstly, arming at the deficiency of OO-Solid model in the aspect of dynamic updating,modeling primitives of OO-Solid model were modified.And then the algorithms of dynamic updating of 3D geological model with the node data,line data or surface data change were dis- cussed.The core algorithms was done by establishing space index,following the way of facing the object from bottom to top,namely the dynamic updating from the node to arc, and then to polygon,then to the face of the component and finally to the geological object. The research has important theoretical and practical values in the field of three dimen- sional geological modeling and is significant in the field of mineral resources.

Three-dimensional numerical modeling of gravity anomalies based on Poisson equation in spacewavenumber mixed domain

In gravity-anomaly-based prospecting, the computational and memory requirements for practical numerical modeling are potentially enormous. Achieving an efficient and precise inversion for gravity anomaly imaging over large-scale and complex terrain requires additional methods. To this end, we have proposed a new topography-capable By performing a two-dimensional Fourier transform in the horizontal directions, threedimensional partial differential equations in the spatial domain were transformed into a group of independent, one-dimensional differential equations engaged with different wave numbers. These independent differential equations are highly parallel across different wave numbers. differential equations with different wave numbers, and the efficiency of solving fixedbandwidth linear equations was further improved by a chasing method. In a synthetic test, a prism model was used to verify the accuracy and reliability of the proposed algorithm by comparing the numerical solution with the analytical solution. We studied the computational precision and efficiency with and without topography using different Fourier transform methods. The results showed that the Guass-FFT method has higher numerical precision, while the standard FFT method is superior, in terms of computation time, for inversion and quantitative interpretation under complicated terrain.

Three-dimensional cell culture systems as an in vitro platform for cancer and stem cell modeling

Three-dimensional(3D)culture systems are becoming increasingly popular due to their ability to mimic tissue-like structures more effectively than the monolayer cultures.In cancer and stem cell research,the natural cell characteristics and architectures are closely mimicked by the 3D cell models.Thus,the 3D cell cultures are promising and suitable systems for various proposes,ranging from disease modeling to drug target identification as well as potential therapeutic substances that may transform our lives.This review provides a comprehensive compendium of recent advancements in culturing cells,in particular cancer and stem cells,using 3D culture techniques.The major approaches highlighted here include cell spheroids,hydrogel embedding,bioreactors,scaffolds,and bioprinting.In addition,the progress of employing 3D cell culture systems as a platform for cancer and stem cell research was addressed,and the prominent studies of 3D cell culture systems were discussed.

Three-dimensional fuzzy logic system for process modeling and control

The traditional fuzzy logic system （FLS） can only model and control the process in two-dimensional nature. Many of real-world systems are of multidimensional features, such as, thermal and fluid processes with spatiotemporal dynamics, biological systems, or decision-making processes that contain stochastic and imprecise uncertainties. These types of systems are difficult for the traditional FLS to model and control because they require a third dimension for spatial or probabilistic information. The type-2 fuzzy set provides the possibility to develop a three-dimensional fuzzy logic system for modeling and controlling these processes in three-dimensional nature.

In vitro three-dimensional cancer metastasis modeling:Past,present,and future

Metastasis is the leading cause of most cancer deaths, as opposed to dysregulated cell growth of the primary tumor. Molecular mechanisms of metastasis have been studied for decades and the findings have evolved our understanding of the progression of malignancy. However, most of the molecular mechanisms fail to address the causes of cancer and its evolutionary origin, demonstrating an inability to find a solution for complete cure of cancer. After being a neglected area of tumor biology for quite some time, recently several studies have focused on the impact of the tumor microenvironment on cancer growth. The importance of the tumor microenvironment is gradually gaining attention, particularly from the per- spective of biophysics. In vitro three-dimensional （3-D） metastatic models are an indispensable platform for investigating the tumor microenvironment, as they mimic the in vivo tumor tissue. In 3-D metastatic in vitro models, static factors such as the mechanical properties, biochemical factors, as well as dynamic factors such as cell-cell, cell-ECM interactions, and fluid shear stress can be studied quantitatively. With increasing focus on basic cancer research and drug development, the in vitro 3-D models offer unique advantages in fundamental and clinical biomedical studies.

Research on the Three-dimensional Modeling and Optimization of a Virtual Tower Crane Based on 3DS Max, Solidworks and EON Professional

Three-dimensional modeling of virtual hoisting machinery is the critical works to structure the system of virtual construction, and the foundation to realize intelligent and interactive virtual hoisting. Aimed at enhancing the requests of image quality and stability of the virtual construction scene, taking a tower crane for example. We studied the technology of three-dimensional modeling and optimization of a virtual tower crane, and a method named two-stage model optimization was put forward. This depended on the modeling stage using Solidworks and 3DS Max and the performance optimization stage in EON. The practice of software development indicates that the proposed methods of three-dimensional modeling and optimization could satisfy the performance request of virtual construction system and be popularized to other virtual system.

Three-Dimensional Pressure Modeling of South China Sea in High Temperature High Pressure Field

Yingqiong basin is a proven hydrocarbon-rich basin in South China Sea. There are a number of large exploration prospects in high temperature and over-pressured formations, especially in Yacheng Block of Qiongdongnan basin and Dongfang District of Yinggehai Basin. Owing to good exploration situation, we have already achieved proven geological reserves over 1000 × 108 m3. In recent years, a few drilled HPHT wells have confirmed that pressure predicted by conventional method was wildly inaccurate. From the view of regional stress, the accuracy of the pressure prediction will be substantially improved. Accurate pressure prediction and three-dimensional pressure modeling which are based on three-dimensional lithology modeling are the cornerstone to achieve exploration breakthrough. In this paper, the use of the triple constraint trend lithology model broke through the traditional method of seismic lithology prediction only by means of impedance threshold value. Compared with actual data and prediction, it confirms that three-dimensional pressure modeling method is reasonable and effective, and has a wide prospect of application.

Fractal analysis of major faults and fractal dimension of lineaments in the Indo-Gangetic Plain on a regional scale

The Indo-Gangetic Plain(IGP)is one of the most seismically vulnerable areas due to its proximity to the Himalayas.Geographic information system(GIS)-based seismic characterization of the IGP was performed based on the degree of deformation and fractal dimension.The zone between the Main Boundary Thrust(MBT)and the Main Central Thrust(MCT)in the Himalayan Mountain Range(HMR)experienced large variations in earthquake magnitude,which were identified by Number-Size(NS)fractal modeling.The central IGP zone experienced only moderate to low mainshock levels.Fractal analysis of earthquake epicenters reveals a large scattering of earthquake epicenters in the HMR and central IGP zones.Similarly,the fault fractal analysis identifies the HMR,central IGP,and south-western IGP zones as having more faults.Overall,the seismicity of the study region is strong in the central IGP,south-western IGP,and HMR zones,moderate in the western and southern IGP,and low in the northern,eastern,and south-eastern IGP zones.

Numerical Investigation of Thermal Behavior of CNC Machine Tool and Its Effects on Dimensional Accuracy of Machined Parts

The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.

基于一种距离相关的超高维生存数据Model-Free特征筛选

随着大数据时代的来临,数据维度爆炸式增长,超高维数据的降维问题逐渐成为众多研究领域的热点话题。由于响应变量通常存在右删失,处理超高维完全数据的降维方法在右删失数据中将不再适用。本研究提出一种新的基于距离相关能有效处理超高维右删失数据的特征筛选方法。首先利用距离相关系数计算每个协变量对响应变量的边际效应,建立与该系数有关的筛选指标,然后再根据事先确立的筛选准则进行特征筛选。提出的特征筛选方法不依赖任何模型结构假定,因此可以有效避免模型指定错误带来的不良后果。此外,该方法采用的距离协方差估计量是总体距离协方差的一个无偏估计,统计准确性和计算精度高。模拟和实证研究表明,提出的方法能在保留所有重要变量的前提下快速剔除与响应变量相关程度较弱的协变量,从而达到降低参数维数的目的。

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

Fixed-Bed Column Adsorption Modeling of MnO4- Ions from Acidic Aqueous Solutions on Activated Carbons Prepared with the Biomass

Activated carbons calcined at 400˚C and 600˚C (AC-400 and AC-600), prepared using palm nuts, collected in the town of Franceville in Gabon, were used to study the dynamic adsorption of MnO4- ions in acidic media on fixed bed column and on the kinetic modeling of experimental data of breakthrough curves of MnO4- ions obtained. Results on the adsorption of MnO4- ions in fixed-bed dynamics obtained on AC-400 and AC-600 adsorbents beds indicated that the AC-400 bed appears to be the most efficient in removing MnO4- ions in acidic media. Indeed, the adsorbed amounts, the adsorbed capacities at saturation and the elimination percentage of MnO4- ions obtained with AC-400 (31.24 mg;52.06 mg·g-1 and 41.65% respectively) were higher compared to those obtained with AC-600 (9.87 mg;16.45 mg·g-1 and 17.79% respectively). The breakthrough curves kinetic modeling revealed that the Thomas model and the pseudo-first-order kinetic model were the most suitable models to describe the adsorption of MnO4- ions on adsorbents studied in our experimental conditions. The results of the intraparticle diffusion model showed that intraparticle diffusion was involved in the adsorption mechanism of MnO4- ions on investigated adsorbents and was not the limiting step and the only process controlling MnO4- ions adsorption. In contrast to AC-400, the intraparticle diffusion on AC-600 bed plays an important role in the adsorption mechanism of MnO4- ions.

Locating Error Considering Dimensional Errors Modeling for Multistation Manufacturing System

Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.

Modeling and validating three dimensional human normal cervix and cervical cancer tissues in vitro

The use of three dimensional in vitro systems in cancer research is a promising path for developing effective anticancer therapies.The aim of this study was to engineer a functional 3-D in vitro model of normal and cancerous cervical tissue.Normal epithelial and immortalized cervical epithelial carcinoma cell lines were used to construct 3-D artificial normal cervical and cervical cancerous tissues.De-epidermised dermis（DED） was used as a scaffold for both models.Morphological analyses were conducted by using hematoxylin and eosin staining and characteristics of the models were studied by analyzing the expression of different structural cytokeratins and differential protein marker MAX dimerisation protein 1（Mad1） using immunohistochemical technique.Haematoxylin and eosin staining results showed that normal cervical tissue had multi epithelial layers while cancerous cervical tissue showed dysplastic changes.Immunohistochemistry staining revealed that for normal cervix model cytokeratin 10 was expressed in the upper stratified layer of the epithelium while cytokeratin 5 was expressed mainly in the middle and basal layer.Cytokeratin 19 was weakly expressed in a few basal cells.Cervical cancer model showed cytokeratin 19 expression in different epithelial layers and weak or no expression for cytokeratin 5 and cytokeratin 10.Madl expression was detected in some suprabasal cells.The 3-D in vitro models showed stratified epithelial layers and expressed the same types and patterns of differentiation marker proteins as seen in corresponding in vivo tissue in either normal cervical or cervical cancerous tissue.These findings imply that they can serve as functional normal and cervical cancer models.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.