A method for improving the accuracy of data mining classification algorithms

In this paper we introduce a method called CL.E.D.M. (CLassification through ELECTRE and Data Mining), that employs aspects of the methodological framework of the ELECTRE I outranking method, and aims at increasing the accuracy of existing data mining classification algorithms. In particular, the method chooses the best decision rules extracted from the training process of the data mining classification algorithms, and then it assigns the classes that correspond to these rules, to the objects that must be classified. Three well known data mining classification algorithms are tested in five different widely used databases to verify the robustness of the proposed method.     

A level-set-based topology and shape optimization method for continuum structure under geometric constraints

Recent advances in level-set-based shape and topology optimization rely on free-form implicit representations to support boundary deformations and topological changes. In practice, a continuum structure is usually designed to meet parametric shape optimization, which is formulated directly in terms of meaningful geometric design variables, but usually does not support free-form boundary and topological changes. In order to solve the disadvantage of traditional step-type structural optimization, a unified optimization method which can fulfill the structural topology, shape, and sizing optimization at the same time is presented. The unified structural optimization model is described by a parameterized level set function that applies compactly supported radial basis functions (CS-RBFs) with favorable smoothness and accuracy for interpolation. The expansion coefficients of the interpolation function are treated as the design variables, which reflect the structural performance impacts of the topology, shape, and geometric constraints. Accordingly, the original topological shape optimization problem under geometric constraint is fully transformed into a simple parameter optimization problem; in other words, the optimization contains the expansion coefficients of the interpolation function in terms of limited design variables. This parameterization transforms the difficult shape and topology optimization problems with geometric constraints into a relatively straightforward parameterized problem to which many gradient-based optimization techniques can be applied. More specifically, the extended finite element method (XFEM) is adopted to improve the accuracy of boundary resolution. At last, combined with the optimality criteria method, several numerical examples are presented to demonstrate the applicability and potential of the presented method.     

Support optimization in additive manufacturing for geometric and thermo-mechanical constraints

Structural and Multidisciplinary Optimization - Supports are often required to safely complete the building of complicated structures by additive manufacturing technologies. In particular, supports...     

提高传感系统准确度的软件方法及其应用

传感系统的2项主要误差是非线性误差和重复性,为了提高传感系统的准确度,提出了减小这2项误差的软件方法:即用神经网络技术减小非线性; 用微机软件数字滤波技术减小重复性.与传统技术比,系统的量程扩大30%以上,非线性减小到0.1%FS以下,重复性也大幅减小,应用效果显著.     

A proposal for improving the accuracy of linguistic modeling

We propose accurate linguistic modeling, a methodology to design linguistic models that are accurate to a high degree and may be suitably interpreted. This approach is based on two main assumptions related to the interpolative reasoning developed by fuzzy rule-based systems: a small change in the structure of the linguistic model based on allowing the linguistic rule to have two consequents associated; and a different way to obtain the knowledge base based on generating a preliminary fuzzy rule set composed of a large number of rules and then selecting the subset of them best cooperating. Moreover, we introduce two variants of an automatic design method for these kinds of linguistic models based on two well-known inductive fuzzy rule generation processes and a genetic process for selecting rules. The accuracy of the proposed methods is compared with other linguistic modeling techniques with different characteristics when solving of three different applications     

A meta-heuristic approach for improving the accuracy in some classification algorithms

Current classification algorithms usually do not try to achieve a balance between fitting and generalization when they infer models from training data. Furthermore, current algorithms ignore the fact that there may be different penalty costs for the false-positive, false-negative, and unclassifiable types. Thus, their performance may not be optimal or may even be coincidental. This paper proposes a meta-heuristic approach, called the Convexity Based Algorithm (CBA), to address these issues. The new approach aims at optimally balancing the data fitting and generalization behaviors of models when some traditional classification approaches are used. The CBA first defines the total misclassification cost (TC) as a weighted function of the three penalty costs and the corresponding error rates as mentioned above. Next it partitions the training data into regions. This is done according to some convexity properties derivable from the training data and the traditional classification method to be used in conjunction with the CBA. Next the CBA uses a genetic approach to determine the optimal levels of fitting and generalization. The TC is used as the fitness function in this genetic approach. Twelve real-life datasets from a wide spectrum of domains were used to better understand the effectiveness of the proposed approach. The computational results indicate that the CBA may potentially fill in a critical gap in the use of current or future classification algorithms.     

Boundary analysis and geometric completion for recognition of interacting machining features

Features are the basic elements which transform CAD data into instructions necessary for automatic generation of manufacturing process plans. In this paper, a hybrid of graph-based and hint-based techniques is proposed to automatically extract interacting features from solid models. The graph-based hints generated by this approach are in geometrical and topological compliance with their corresponding features. They indicate whether the feature is 2.5D, floorless or 3D. To reduce the product model complexity while extracting features, a method to remove fillets existing in the boundary of a 2.5D feature is also proposed. Finally, three geometric completion algorithms, namely, Base-Completion, Profile-Completion and 3D-volume generation algorithms are proposed to generate feature volumes. The base-completion and profile-completion algorithms generate maximal volumes for 2.5D features. The 3D volume generation algorithm extracts 3D portions of the part.     

A method for computer-aided specification of geometric tolerances

The paper describes a method for the generation of tolerance specifications from product data. The problem is nontrivial due to the increasing adoption of geometric dimensioning criteria, which call for the use of many types of geometric tolerances to completely and unambiguously represent the design intent and the many constraints deriving from manufacturing, assembly and inspection processes. All these issues have to be modeled and explicitly provided to a generative specification procedure, which may thus need a large amount of input data. The proposed approach tries to avoid this difficulty by considering that most precision requirements to be defined relate to the assembly process, and can be automatically derived by analyzing the contact relations between parts and the assembly operations planned for the product. Along with possible user-defined additional requirements relating to function, assembly requirements are used in a rule-based geometric reasoning procedure to select datum reference frames for each part and to assign tolerance types to part features. A demonstrative software tool based on the developed procedure has allowed to verify its correctness and application scope on some product examples.     

Introducing a level-set based shape and topology optimization method for the wear of composite materials with geometric constraints

The wear of materials continues to be a limiting factor in the lifetime and performance of mechanical systems with sliding surfaces. As the demand for low wear materials grows so does the need for models and methods to systematically optimize tribological systems. Elastic foundation models offer a simplified framework to study the wear of multimaterial composites subject to abrasive sliding. Previously, the evolving wear profile has been shown to converge to a steady-state that is characterized by a time-independent elliptic equation. In this article, the steady-state formulation is generalized and integrated with shape optimization to improve the wear performance of bi-material composites. Both macroscopic structures and periodic material microstructures are considered. Several common tribological objectives for systems undergoing wear are identified and mathematically formalized with shape derivatives. These include (i) achieving a planar wear surface from multimaterial composites and (ii) minimizing the run-in volume of material lost before steady-state wear is achieved. A level-set based topology optimization algorithm that incorporates a novel constraint on the level-set function is presented. In particular, a new scheme is developed to update material interfaces; the scheme (i) conveniently enforces volume constraints at each iteration, (ii) controls the complexity of design features using perimeter penalization, and (iii) nucleates holes or inclusions with the topological gradient. The broad applicability of the proposed formulation for problems beyond wear is discussed, especially for problems where convenient control of the complexity of geometric features is desired.     

A surface based approach to recognition of geometric features for quality freeform surface machining

The paper presents a surface-based approach for geometric feature recognition for the purpose of automating the process planning of freeform surface machining. The proposed approach consists of the following four steps for recognition of the geometric features: conversion and preprocessing of the surface geometry data, subdivision of NURBS surface, reconstruction of surface orientation areas, and recognition of geometric features. The proposed scheme assumes that the input geometry data form is based on an IGES CAD model and the surface model can be represented in the form of trimmed NURBS surfaces. The connectivity relations of the product surfaces are analyzed and each surface is subdivided into orientation regions based on the surface normal vector over a certain point density grid, and then all the connected regions with the same orientation type are grouped into surface orientation areas. After that, the geometric feature will be recognized through the analysis of area connectivity and relationship. The paper describes the developed algorithms on surface orientation region subdivision, surface orientation area reconstruction, and geometric feature recognition. The verified feasibility study of the developed method is also presented.     

A feature-based method for NC machining time estimation

Machining time estimation plays an important role in manufacturing process planning and scheduling. Existing NC machining time estimation methods are all based on material removal rates, NC programs, and machine characteristics. However, the machining condition which is related to the geometry-process information is also an important impact factor of the NC machining time estimation. As existing methods cannot satisfy the requirement of timeliness, accuracy and efficiency, this paper presents a feature-based method for NC machining time estimation. Experiment results show that the proposed approach is feasible and practical. It is particularly useful in real time manufacturing process planning and scheduling systems.     

A human-assisted knowledge extraction method for machining operations

This paper deals with a human-assisted knowledge extraction method to extract “if…then…” rules from a small set of machining data. The presented method utilizes both probabilistic reasoning and fuzzy logical reasoning to benefit from the machining data and from the judgment and preference of a machinist. Using the extracted rules, one can determine the values of operational parameters (feed, cutting velocity, etc.) to ensure the desired machining performance (keep surface roughness within the stipulated range (e.g., moderate)). Applying the presented method in a real-life machining knowledge extraction situation and comparing it with the inductive learning based knowledge extraction method (i.e., ID3), the usefulness of the method is demonstrated. As the concept of manufacturing automation is shifting toward “how to support humans by computers”, the presented method provides some valuable hints to the developers of futuristic computer integrated manufacturing systems.     

A hybrid approach for improving predictive accuracy of collaborative filtering algorithms

Recommender systems represent a class of personalized systems that aim at predicting a user’s interest on information items available in the application domain, operating upon user-driven ratings on items and/or item features. One of the most widely used recommendation methods is collaborative filtering that exploits the assumption that users who have agreed in the past in their ratings on observed items will eventually agree in the future. Despite the success of recommendation methods and collaborative filtering in particular, in real-world applications they suffer from the insufficient number of available ratings, which significantly affects the accuracy of prediction. In this paper, we propose recommendation approaches that follow the collaborative filtering reasoning and utilize the notion of lifestyle as an effective user characteristic that can group consumers in terms of their behavior as indicated in consumer behavior and marketing theory. Emanating from a basic lifestyle-based recommendation algorithm we incrementally proceed to the development of hybrid recommendation approaches that address certain dimensions of the sparsity problem and empirically evaluate them providing further evidence of their effectiveness.     

A linear hybrid methodology for improving accuracy of time series forecasting

Modeling and forecasting of time series data are integral parts of many scientific and engineering applications. Increasing precision of the performed forecasts is highly desirable but a difficult task, facing a number of mathematical as well as decision-making challenges. This paper presents a novel approach for linearly combining multiple models in order to improve time series forecasting accuracy. Our approach is based on the assumption that each future observation of a time series is a linear combination of the arithmetic mean and median of the forecasts from all participated models together with a random noise. The proposed ensemble is constructed with five different forecasting models and is tested on six real-world time series. Obtained results demonstrate that the forecasting accuracies are significantly improved through our combination mechanism. A nonparametric statistical analysis is also carried out to show the superior forecasting performances of the proposed ensemble scheme over the individual models as well as a number of other forecast combination techniques.     

On the accuracy of shape sensitivity

The calculation of sensitivity of the response of a structure modeled by finite elements to shape variation is known to be subject to numerical difficulties. The accuracy of a given method is typically measured against the yard stick of finite-difference sensitivity calculation. The present paper demonstrates with a simple example that this approach may be flawed because of discretization errors associated with the finite element mesh. Seven methods for calculating sensitivity derivatives are compared for a two-material beam problem with a moving interface. It is found that as the mesh is refined, displacement sensitivity derivatives converge more slowly than the displacements. Six of the methods agree fairly well, but the adjoint variational surface method provides substantially different results. However, the difference is found to reflect convergence from another direction to the same answer rather than reduced accuracy. Additionally, it is observed that small derivatives are particularly prone to accuracy problems.     

A unified framework for improving the accuracy of all holistic face identification algorithms

Reconstructing the challenging human face identification process as a stability problem, we show that Electoral College can be used as a framework that provides a significantly enhanced face identification process by improving the accuracy of all holistic algorithms. The results are demonstrated by extensive experiments on benchmark face databases applying the Electoral College framework embedded with standard baseline and newly developed face identification algorithms.     

Computer numerical control for assuring the machining accuracy of contour milling

In this paper, a software technology for improving the machining accuracy in contour milling is discussed, in which the continuous path control is thoroughly investigated from the viewpoint of system synthesis, and the computer numerical control is effectively used. It is shown that the proposed “real-time cutter path rectification” offers an effective means to overcome the serious problem of the thermal deformation of workpieces. In this case, it is necessary to take many factors into consideration; the diversity of shapes, the change of cutting conditions, the unstable thermal situation, and so on. Therefore, the adaptive control is applied to compensate the thermal displacement of the contour during the cutting process. Relating to this subject, the effective cutter radius, which depends on cutter wear, is also evaluated in real-time operation; and the cutter diameter compensation is included in the “cutter path rectification”. In order to assure the machining accuracy, a new approach to contour measurement is proposed, in which the continuous path control by CNC system is used. It is certified through some experiments that the method proposed in this paper is useful to realize the flexible automation with high machining accuracy.