An approach for feature semantics recognition in geometric models

This paper describes a method for the recognition of the semantics of parts (features) of a component from a pure geometric representation. It is suitable for verifying product life-cycle requirements from the early stages of the design process. The proposed method is appropriate to analyse B-rep geometric models, and it is not limited to models described by planar and cylindrical surfaces, but it can handle several types of face shapes. In this work the concept of semanteme is introduced. A semanteme represents the minimal element of engineering meaning that can be recognised in a geometric model. The semantemes recognised in a part of the model, which are potentially of engineering significance, are used to associate an engineering meaning to the part. This approach gives a wide flexibility to the proposed system, which is suitable to be used in different contexts of application, since it is possible to describe the reference context using the semanteme that the system can manage.In the paper the implemented prototype system is briefly described. The prototype system takes advantage of neutral interfaces that allow geometrical and topological information to be retrieved from a commercial CAD system.     

A graph-based expert system approach to geometric feature recognition

Geometric feature recognition is a crucial task in the development of concurrent engineering software. This paper presents a new methodology for geometric feature recognition which combines the advantages of face-edge adjacency graphs and expert systems. The methodology uses several new concepts such as enhanced winged edge data structure (EWEDS) and multi-attributed adjacency graphs (MAAG). The object model is presented as a set of facts. The rules for the recognition of each feature are derived from the corresponding feature-MAAG. This simplifies the process of writing the rules while enabling the inclusion of new features into the rule base as they are encountered.     

An adaptive process planning approach of rapid prototyping and manufacturing

This paper presents an adaptive approach to improve the process planning of Rapid Prototyping/Manufacturing (RP/M) for complex product models such as biomedical models. Non-Uniform Rational B-Spline (NURBS)-based curves were introduced to represent the boundary contours of the sliced layers in RP/M to maintain the geometrical accuracy of the original models. A mixed tool-path generation algorithm was then developed to generate contour tool-paths along the boundary and offset curves of each sliced layer to preserve geometrical accuracy, and zigzag tool-paths for the internal area of the layer to simplify computing processes and speed up fabrication. In addition, based on the developed build time and geometrical accuracy analysis models, adaptive algorithms were designed to generate an adaptive speed of the RP/M nozzle/print head for the contour tool-paths to address the geometrical characteristics of each layer, and to identify the best slope degree of the zigzag tool-paths towards achieving the minimum build time. Five case studies of complex biomedical models were used to verify and demonstrate the improved performance of the approach in terms of processing effectiveness and geometrical accuracy.     

A practical approach to a process planning expert system for manufacturing processes

In this paper the methodical techniques applied by the human process planning expertise is simulated. It considers the process plans design, or process selection. Software modules are designed to generate a process plan or several plans for a new part according to the input data from its engineering drawing. A specific module for each surface type, to match the surface parametric data and the required quantities with respect to the capability matrices, in order to locate the most eligible process plan is identified and used.     

An artificial intelligence approach to speech recognition and understanding

This paper presents the I.R.S.T. Automatic Speech Recognition and Understanding (A.S.R.U.) Research Program for continuously spoken Italian without previous knowledge of the identity of the speaker. The acoustic analysis is performed in time domain and works in real-time. Acoustic ambiguities are overcome by using various levels of contextual information (orthophonic, syntactic, semantic) to formulate hypotheses to be verified by means of an hypothesize and test paradigm. The architecture is an analysis by synthesis loop.     

An application-independent methodology of feature recognition with explicit representation of feature interaction

The existing feature-based design and feature recognition methods cannot fulfil the requirements of automated process planning. It is now recognized that satisfactory modelling of interactions between features is necessary for developing an automated process planning system. The selection of an optimum manufacturing process for a part needs to be considered at the conceptual design phase to incorporate the capabilities and constraints of the process in design. This paper describes a methodology of feature recognition that is independent of manufacturing process and explicitly generates geometric feature interactions in a part. The paper illustrates generation of feature sets for shape-forming processes, and describes a method to convert the process-independent features into machinable volumes and tool paths for material removal processes.     

The artificial intelligence approach to pattern recognition—a perspective and an overview

The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient issues in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.     

Hybrid artificial intelligence approach to urban planning

Knowledge-based modeling and implementation of the various urban planning processes represent an intensive research area. This paper presents a hybrid artificial intelligence system using a knowledge-based approach, neural networks and fuzzy logic that automates the decision-making process in urban planning. The system is used for developing urban development alternatives based on real-world data. Results show that, by integrating knowledge-based systems, artificial neural networks and fuzzy systems, the system achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application. With this approach, the best of three technologies can be compiled together to solve complex urban problems. We discuss the structure of the combined technologies, as well as providing examples of its application in the field of urban development.     

High-level feature recognition using feature relationship graphs

In this paper, we present a method to extract user-specific features from common features. This contrasts with other approaches which work directly off B-rep geometric models. Here, user-specific features are called high-level features which are a set of common features combined in a user-specific manner. A feature relationship graph is used to organize common features in a part and to define high-level feature patterns. The research presented in this paper focuses mainly on feature relationship graph construction and high-level feature recognition using subgraph isomorphic techniques.     

An integration of feature recognition and process planning functions for turning operation

This paper deals with the development of a Computer Aided Process Planning(CAPP) system interfaced with CAD for turning operation. The developed system performs CAD interfaced feature recognition from part geometry database, and the decision procedures for process planning functions such as process and operation selection, machine tool selection, setup planning and operation planning.

The system runs under personal computer environments and AutoCAD is employed as CAD system and Turbo-C is used for the decision procedures. A case study is performed to evaluate the performance of the system.     

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modeled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited.An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain-based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.     

Dealing with feature interactions for prismatic parts in STEP-NC

Determining the precedence of machining features is a critical issue in feature-based process planning. It becomes more complex when geometric interaction occurs between machining features. STEP-NC, the extension of STEP (ISO 10303) standard developed for CNC controllers, is a feature-based data model. It represents all the geometric and topological product data minus feature interactions. In this paper, machining precedence of interactive and non-interactive STEP-NC features is discussed. Local and global precedence of machining features are defined on the basis of geometric constraints, such as geometric interaction of features and feature approach face and technological constraint such as access direction of the cutting tool. A software tool has been developed to visualize the STEP-NC part model and to generate the graphs of feature interaction and feature precedence. The output can be then used to augment the STEP-NC data in order to generate the optimal sequence of operations.     

An artificial intelligence approach to language instruction

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding.

For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input.

The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.     

基于局部特征过滤的快速火焰图像识别方法

传统的基于物理信号的火焰识别方法易被外部环境干扰,且现有火焰图像特征提取方法对于火焰和场景的区分度较低,从而导致火焰种类或场景改变时识别精度降低。针对这一问题,提出一种基于局部特征过滤和极限学习机的快速火焰识别方法,将颜色空间信息引入尺度不变特征变换（SIFT）算法。首先,将视频文件转化成帧图像,利用SIFT算法对所有图像提取特征描述符;其次,通过火焰在颜色空间上的信息特性进一步过滤局部噪声特征点,并借助关键点词袋（BOK）方法,将特征描述符转换成对应的特征向量;最后放入极限学习机进行训练,从而快速得到火焰识别模型。在火焰公开数据集及真实火灾场景图像进行的实验结果表明：所提方法对不同场景和火焰类型均具有较高的识别率和较快的检测速度,实验识别精度达97%以上;对于包含4301张图片数据的测试集,模型识别时间仅需2.19 s;与基于信息熵、纹理特征、火焰蔓延率的支持向量机模型,基于SIFT、火焰颜色空间特性的支持向量机模型,基于SIFT的极限学习机模型三种方法相比,所提方法在测试集精度、模型构建时间上均占有优势。     

Automated inspection process planning: Algorithmic inspection feature recognition, and inspection case representation for CBR

General metrological inspection planning is among the least explored computer-aided process planning (CAPP) domains. This paper explores certain basic issues involved in inspection planning using case-based reasoning in an environment of a Generic CAPP Support System. Firstly, algorithmic methods for characterizing and extracting inspection features are proposed and discussed. A sequential knowledge based filtering method is developed to reduce the number of inspection features typically encountered in metrological inspection planning. Finally, a formalized approach for case representation of relevant inspection domain knowledge using a newly developed parametric-list technological feature graph (PLTFG) is presented.     

Empirical tests for feature selection based on a psychological theory of character recognition

It is argued that machine algorithms based on feature detection promise the greatest chance for success in the recognition of isolated, unconstrained handprinted characters. In order to match human performance, the features used cannot be chosen in an arbitrary manner; they must have some psychological significance. A theory of characters based on functional attributes is reviewed, and three psychophysical tests are described for determining the psychological validity of any postulated attribute. The first test indicates if a particular attribute is involved in a particular letter, and the second and third tests investigate the commonality of an attribute among different letters.     

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.     

A feasible approach to the integration of CAD and CAPP

Although current CAD systems are declared to be feature-based, in fact, the so-called feature is just a modeling macro or menu name such as Protrusion, Revolution, Cutout, Block, etc., instead of a design feature or manufacturing feature in accordance with engineering practice. Consequently, product model data insufficiency and incompatibility between varieties of application systems are still the major barriers to system integration, especially the integration of design and process planning. This paper proposes a practical solution for a bi-directional integration of CAD and CAPP on the platform of commercial CAD systems. The key techniques such as feature recognition and conversion, feature parameter and constraint extraction, feature tree reconstruction, technical information processing, process planning, automatic process drawing marking and 3D material stock CAD model generating are discussed. And the extracted features and their related technical information and knowledge are encapsulated together with the geometry-oriented CAD model to form an integrated product information model to facilitate effective integration with the downstream activities. The integrated CAD/CAPP system is implemented on a commercial CAD package, UGS/SolidEdge. A case study and industry implementation illustrate the feasibility of the approach proposed.     

Hybrid neural network and genetic algorithm based machining feature recognition

In this research, neural networks (NNs) and genetic algorithms (GAs) are used together in a hybrid approach to reduce the computational complexity of feature recognition problem. The proposed approach combines the characteristics of evolutionary technique and NN to overcome the shortcomings of feature recognition problem. Consideration is given to reduce the computational complexity of network with specific interest to design the optimum network architecture using GA input selection approach. In order to evaluate the performance of the proposed system, experimental results are compared with previous NN based feature recognition research.