Incremental Methods Research Articles

Toward a wavefunction-based treatment of strong electron correlation in extended systems by means of incremental methods

Incremental expansions have been used in the past to make electron correlation energies of large molecules and solids accessible to a treatment with wavefunction-based quantum-chemical methods. For this purpose, the Hartree-Fock space of occupied orbitals is partitioned into groups of localized orbitals; these groups are then correlated separately, one at a time, and their correlation energies are added up to a first estimate of the total correlation energy; subsequently, this estimate is systematically improved by correlating pairs, triples, etc., of the localized groups simultaneously. This works well when dynamic correlation effects are predominant and correlation effects rapidly decrease with the distance of the localization centers. For strong static correlation, Hartree-Fock is no longer a good starting point, localization is less well effective, and the convergence of the incremental scheme deteriorates. Here, two ways of improvement are considered for the latter case: optimization of the localized orbitals at a higher level than Hartree-Fock and use of incremental expansions from more than one reference determinant. These improvements are illustrated for H10 rings and H12 sheets up to dissociation and for the transition between the intra-atomic and interatomic bonding regimes in Be6 rings.

Automatyzacja procesu projektowania oraz wytwarzania polimerowych modeli struktur anatomicznych zuchwy w konwencji Przemysl 4.0

An example of the application of the Industry 4.0 concept in the process of designing and producing polymeric models of mandible anatomical structures has been presented here. Automating the process of making surgical templates based on the Industry 4.0 structure is aimed at quickly providing physicians with ready-made tools to improve the performance of surgical procedures. Integrating the reconstruction engineering (RE), computer aided design (CAD) and incremental rapid prototyping methods (RP) in the process of designing and manufacturing anatomical models may not only shorten the preparation time of surgical template, but also increase the accuracy of its production. On the basis of the data provided by the F. Chopin Regional Hospital No. 1 in Rzeszow, 14 patients with mandible injuries were examined. The obtained methodology has confirmed that the application of the Industry 4.0 concept in the process of making models of anatomical structures can bring many benefits.

Comparative Analysis of Non-linear Periodic Response of Cross-ply and Angle-ply Laminated Composite Plates

Non-linear periodic response periodic response analysis of laminated structures is necessary for their optimal and safe dynamic design. The present work is intended to explore the influence of lamination scheme and fibre-orientation on the nonlinear forced vibration response of laminated plates. The analysis has been carried out using finite element. In present work, a comparative analysis of forced vibration response between cross-ply and angle-ply laminated composite plates have been explored. The effects of fiber orientation on non-linear forced vibration response characteristics have been analyzed. This finite element analysis has been done using shooting method along with continuation scheme in the time domain. The method employed is capable to get the complete stable and unstable branches past bifurcations. The dynamic behavior is explored using the temporal history of the response and phase plane plots. The method presented in the paper is computationally efficient and does not require apriori assumption on the participating modes, unlike harmonic balance and incremental harmonic balance methods.

Open Set Incremental Learning for Automatic Target Recognition

Incremental learning methods update the existing model with new knowledge when the target data increase continuously. Open set recognition (OSR) algorithms provide classifiers with a rejection option so that the new untrained target type is identified. In this paper, an open set incremental learning method is introduced for automatic target recognition, which is able to recognize and learn the new unknown classes continually. The proposed method, open set model with incremental learning (OSmIL), is an ensemble classifier so it is able to be updated only by the new data. For saving the computational time and storage source, a new exemplar selection method is introduced for model simplifying. Edge samples are selected to cover training classes; as a result, the model size is deduced and controlled. Moreover, because extreme value theory (EVT) is suitable to fit a classification model that includes open space risk, the decision function based on EVT makes an open set classifier for identifying the new classes. Experimental results demonstrate that the proposed OSmIL outperforms the other state of the arts on the accuracy of multiclass OSR. And OSmIL can maintain good accuracy and efficiency in the incremental learning experiment set.

Design and Implementation of Oracle Database Incremental Data Capture Based on Trigger and Identification Table

Data synchronization technology is one of the key technologies of data storage in distributed database. At present, it is an effective method to achieve data synchronization between source database and target database by capturing incremental data and transferring incremental files. Based on the research of Oracle database and the analysis of commonly using incremental data capture methods, this paper designed DML trigger and incremental data identification table to store incremental identification data in the identification table, and generated incremental XML files for network transmission according to the identification table. Finally, the feasibility of this method was tested. Tests showed that the method was reliable and stable, and could be applied to data synchronization in distributed databases.

Data-Driven Microstructure Property Relations

An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and, thereafter, to compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available.

From anomaly detection to rumour detection using data streams of social platforms

Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach. In addition, we propose incremental methods, which enable us to detect rumours using streaming data of social platforms. We illustrate the effectiveness and efficiency of our approach with a real-world dataset of 4M tweets with more than 1000 rumours.

Related families-based methods for updating reducts under dynamic object sets

Due to rapid growth of data with respect to time, feature selection in dynamic covering decision information systems (DCDISs) is an important research direction of covering rough set theory, and we have not observed researches on related families-based methods for updating reducts of DCDISs with dynamic object variations. In this paper, we first introduce the concepts of covering decision approximation spaces (CDASs) and dynamic covering decision approximation spaces (DCDASs) when varying object sets, and illustrate the relationship between related sets of CDASs and those of DCDASs. Then incremental learning methods based on related families are provided for feature selection in DCDISs with the addition and deletion of objects. Finally, we develop the corresponding heuristic incremental algorithms for feature selection in DCDISs and employ experimental results on benchmark datasets to demonstrate that these algorithms give satisfactory results in terms of running times.

Research on Two Main Construction Methods of Concept Lattices

Because of the completeness of concept lattices, the time complexity of constructing concept lattices has become the main factor affecting the application of formal concept analysis (FCA). The key problems in the research of concept lattices are how to improve the generation efficiency and how to reduce the space and time complexity of constructing concept lattices. So far, reviews on lattice construction algorithms have not been comprehensive. In view of this situation, we give a detailed review on two categories of construction algorithms: batch methods and incremental methods. The first category is a formal context that cannot be updated once the concept lattice has been constructed; the second category is a formal context that can be updated after a new object being added to the formal context. We briefly introduce classical and improved construction methods, illustrate the deficiencies of some algorithms and point out the improvements of the follow-up algorithms. Furthermore, we compare and discuss several key algorithms, and also pay attention to the application of concept lattices. Finally, two further research directions of concept lattices are proposed, including parallel construction methods of concept lattices and research of heterogeneous data concept lattices.

Incremental fuzzy clustering based on a fuzzy scatter matrix

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and between-cluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

One-step inverse isogeometric analysis for the simulation of sheet metal forming

Isogeometric analysis (IGA) has been used with great success when combined with incremental methods to simulate sheet metal forming. In this paper, we present the development of one-step inverse IGA based on the total deformation theory of plasticity. For a large number of industrial stamping parts, the membrane effects are dominant. Thus, we adopted an isogeometric membrane element to predict the flattened contour of the initial blank from the energy-based initial solution estimation approach. In addition, we used the Newton–Raphson algorithm for nonlinear plastic iterations to evaluate the thickness and equivalent strain and stress of the final stamping parts. We applied our framework to square box and S-rail surface models for demonstration. The results for these two examples illustrate the performance of one-step inverse IGA and its applicability to the integrated design of sheet metal forming.

A Rapid Analysis Procedure for Estimating the Seismic Collapse Capacity of Moment Resisting Frames

ABSTRACT A new rapid analysis method for estimating the seismic collapse capacity of moment-resisting frames is proposed. Numerous generic structures with different structural properties are used to model the moment-resisting frames. A database of structural responses for moment-resisting frames is constructed by nonlinear static and incremental dynamic analysis methods. Using the database, a closed-form equation for predicting the median collapse capacity of moment-resisting frames is then developed. The proposed method is assessed by comparing its collapse capacity predictions on 57 different moment-resisting frames with those obtained from incremental dynamic analyses. The results evidently show that the proposed methodology is reliable.

Cost-Sensitive Weighting and Imbalance-Reversed Bagging for Streaming Imbalanced and Concept Drifting in Electricity Pricing Classification

In data streaming environments such as a smart grid, it is impossible to restrict each data chunk to have the same number of samples in each class. Hence, in addition to the concept drift, classification problems in streaming data environments are inherently imbalanced. However, streaming imbalanced and concept drifting problems in the power system and smart grid have rarely been studied. Incremental learning aims to learn the correct classification for the future unseen samples from the given streaming data. In this paper, we propose a new incremental ensemble learning method to handle both concept drift and class imbalance issues. The class imbalance issue is tackled by an imbalance-reversed bagging method that improves the true positive rate while maintains a low false positive rate. The adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers according to their classification performances and stochastic sensitivities. The proposed method is applied to a case study for the electricity pricing in Australia to predict whether the price of New South Wales will be higher or lower than that of Victorias in a 24-h period. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.

Wear evaluation study of the multiple grooved pulleys with optical method

Multiple grooved pulley wear can be evaluated by using two methods, coordinate method and optical method. This paper presents the results of the research and the analysis of the geometrical features for this type of pulleys, which was obtained with the ATOS II optical system. In this method the evaluation of wear is made based on the comparison of the manufactured parts or assemblies with superimposing the CAD model and the surface model obtained from digitization. Existing differences can be easily determined by their visualization in the form of a colored map of deviations or profile differences. This method is subject to a methodological error, which is a result of the accuracy of the scan and the correctness of the model's fit. Optical techniques can be used to determine the accuracy of the geometric features for products made with any shaping technique (machining, plastic processing, incremental methods, etc.).

Vector Penalty-Projection Methods for Open Boundary Conditions with Optimal Second-Order Accuracy

Recently, a new family of splitting methods, the so-called vector penalty-projection methods (VPP) were introduced by Angot et al. [2, 3] to compute the solution of unsteady incompressible fluid flows and to overcome most of the drawbacks of the usual incremental projection methods. Two different parameters are related to the VPP methods: the augmentation parameter r ≥ 0 and the penalty parameter 0 < e ≤ 1. In this paper, we deal with the time-dependent incompressible Navier-Stokes equations with outflow boundary conditions using the VPP methods. The spatial dis-cretization is based on the finite volume scheme on a Marker and Cells (MAC) staggered grid. Furthermore, two different second-order time discretization schemes are investigated: the second-order Backward Difference Formula (BDF2) known also as Gear's scheme and the Crank-Nicolson scheme. We show that the VPP methods provide a second-order convergence rate for both velocity and pressure in space and time even in the presence of open boundary conditions with small values of the augmentation parameter r typically 0 ≤ r ≤ 1 and a penalty parameter e small enough typically e = 10 −10. The resulting constraint on the discrete divergence of velocity is not exactly equal to zero but is satisfied approximately as O(e δt) where e is the penalty parameter (taken as small as desired) and δt is the time step. The choice r = 0 requires a special attention to avoid the accumulation of round-off errors for very small values of e. Indeed, it is important in this case to directly correct the pressure gradient by taking account of the velocity correction issued from the vector penalty-projection step. Finally, the efficiency and the second-order accuracy of the method are illustrated by several numerical test cases including homogeneous or non-homogeneous given traction on the boundary.

Convergence Rate of Incremental Gradient and Incremental Newton Methods

The incremental gradient method is a prominent algorithm for minimizing a finite sum of smooth convex functions, used in many contexts including large-scale data processing applications and distributed optimization over networks. It is a first-order method that processes the functions one at a time based on their gradient information. The incremental Newton method, on the other hand, is a second-order variant which exploits additionally the curvature information of the underlying functions and can therefore be faster. In this paper, we focus on the case when the objective function is strongly convex and present fast convergence results for the incremental gradient and incremental Newton methods under the constant and diminishing stepsizes. For a decaying stepsize rule $\alpha_k = \Theta(1/k^s)$ with $s \in (0,1]$, we show that the distance of the IG iterates to the optimal solution converges at rate ${\cal O}(1/k^{s})$ (which translates into ${\cal O}(1/k^{2s})$ rate in the suboptimality of the objective value). For $s>1/2$, this improves the previous ${\cal O}(1/\sqrt{k})$ results in distances obtained for the case when functions are non-smooth. We show that to achieve the fastest ${\cal O}(1/k)$ rate, incremental gradient needs a stepsize that requires tuning to the strong convexity parameter whereas the incremental Newton method does not. The results are based on viewing the incremental gradient method as a gradient descent method with gradient errors, devising efficient upper bounds for the gradient error to derive inequalities that relate distances of the consecutive iterates to the optimal solution and finally applying Chung's lemmas from the stochastic approximation literature to these inequalities to determine their asymptotic behavior. In addition, we construct examples to show tightness of our rate results.

An Inexact Variable Metric Proximal Point Algorithm for Generic Quasi-Newton Acceleration

We propose an inexact variable-metric proximal point algorithm to accelerate gradient-based optimization algorithms. The proposed scheme, called QNing can be notably applied to incremental first-order methods such as the stochastic variance-reduced gradient descent algorithm (SVRG) and other randomized incremental optimization algorithms. QNing is also compatible with composite objectives, meaning that it has the ability to provide exactly sparse solutions when the objective involves a sparsity-inducing regularization. When combined with limited-memory BFGS rules, QNing is particularly effective to solve high-dimensional optimization problems, while enjoying a worst-case linear convergence rate for strongly convex problems. We present experimental results where QNing gives significant improvements over competing methods for training machine learning methods on large samples and in high dimensions.

An Incremental Group-Specific Framework Based on Community Detection for Cold Start Recommendation

To address cold start problem by utilizing only rating information, this paper proposes an incremental group-specific framework for recommender systems. Firstly, a decoupled normalization method is introduced to extract preference patterns from ratings. Then, two incremental community detection methods are proposed to detect evolving communities in dynamic networks for capturing interest shifts, and new users/items corresponding communities according to the missing mechanism of ratings. Finally, an incremental group-specific model is proposed to incorporate evolving communities for recommender systems. A series of empirical analysis on three datasets is conducted to validate the rationality of grouping new users/items with missingness-related information. Experimental results show that the proposed framework can achieve better performance compared with other competitive methods, and is capable of handling the cold start problem and highly scalable with incremental data.

Rearrangement Public Corridor within Green Concept and Sustainability Case Study of Mayjen Yono Suwoyo Street, Surabaya, Indonesia

Public green open space is a critical component that affects the quality of human life, both ecologically and sociallypsychologically. Surabaya, a metropolitan city in Indonesia, is currently trying to meet the needs of public green open spaces. In the commercial area, the social needs of the Surabaya community towards their environment prove that commercial corridors require sustainable green open space arrangement, where socially sustainable can overcome the saturation in economic activities and also can improve environmental conditions. The method in this study uses case study research methods, synopsis, and incremental design methods. The method requires a systematic approach to the existing conditions of the corridor from the existing cases- preparation of criteria and the concept of sustainable public green open space complement the method. The results of the study are structuring urban space in social activities to reduce the saturation of commercial actors, such as by adding a seating area for relaxation for the community, a play area for recreation and an education area in the area around the pond. This result is following the representation of the criteria and concepts of research and structuring of public green open space, which proves that emphasizing social activities can apply sustainability to the study area. The arrangement is carried out in the median, road roundabout and other elements in the commercial corridor of Mayjen Yono Suwoyo according to several factors and structuring criteria.

Thermo-mechanic tests using 3d printed elements

3D printing is an innovative technology that allows for rapid prototyping of designs and models. The use of different printing methods and materials used allows the creation of both functional objects of real size and scale models. These features make 3D printing its application in virtually all fields of science and industry, allowing for faster design while reducing costs, compared to traditional prototyping techniques. Incremental methods enable quick and economical creation of conceptual and functional models of prototypes composed of several components. There are many methods of FDM, CJP, SLA, SLS, DLP, MJM, DMS. On the basis of the printout, you can create a model that does not differ in appearance from the mass produced. Through this technique, you can create metallized, rubberized, transparent surfaces, brought to an ideal gloss or matt, also having different colors depending on the material used (filament), the material from which the model is made. This article presents the application of printed elements using the FDM method in a research stand for PIV measurements.